Triggered spectroscopic analysis and target identification

Abstract

Systems and methods for identifying an anatomical target during a surgical laser procedure and adjusting treatment accordingly are disclosed. An exemplary surgical laser system includes a laser system to emit laser pulses via a laser fiber to an anatomical target, one or more sensors to sense information from the anatomical target, and a controller circuit to initiate triggered collection of sensor data in a presence of a trigger event indicative of the laser pulses being delivered to the anatomical target, which occurs when the field of view is clear and the laser fiber is properly positioned over the anatomical target. The controller circuit identifies a type or composition of the anatomical target using the triggered collection of sensor data, and adjusts a laser output setting of the laser system based on the result of target identification.

Description

TRIGGERED SPECTROSCOPIC ANALYSIS AND TARGETIDENTIFICATIONPRIORITY CLAIM

[0001] This application claims the benefit of priority to U.S. Provisional Patent Application Serial No. 63 / 617,934, filed lanuary 5, 2024, the contents of which are incorporated herein by reference.TECHNICAL FIELD

[0002] This document relates generally to surgical laser systems, and more specifically relates to a laser endoscopy system for triggered spectroscopic data collection and target identification during an endoscopy procedure.BACKGROUND

[0003] Endoscopes are typically used to provide access to an internal location of a patient so that a doctor is provided with visual access. Some endoscopes are used in minimally invasive surgery to remove unwanted tissue or foreign objects from the body of the patient. For example, a nephroscope is used by a clinician to inspect the renal system, and to perform various procedures under direct visual control. In a percutaneous nephrolithotomy (PCNL) procedure, a nephroscope is placed through the patient’s flank into the renal pelvis. Calculi or mass from various regions of a body including, for example, urinary system, gallbladder, nasal passages, gastrointestinal tract, stomach, or tonsils, can be visualized and extracted.

[0004] Laser or plasma systems have been used for delivering surgical laser energy to various target treatment areas such as soft or hard tissue. Examples of the laser therapy include ablation, coagulation, vaporization, fragmentation, etc. In lithotripsy applications, laser has been used to break down calculi structures in kidney, gallbladder, ureter, among other stone-forming regions, or to ablate large calculi into smaller fragments. In endoscopic laser therapy, it is desirable that lasers be applied only to target treatment structures (e.g., calculi or cancerous tissue), and spare non-treatment tissue from unintended laser irradiation.SUMMARY

[0005] The present document describes systems, devices, and methods for identifying an anatomical target such as a calculi structure or tissue during a medical procedure such as a surgical laser procedure using sensor data indicative of properties of the anatomical target, and automatically adjusting therapy in accordance with the result of target identification. To ensure high quality sensor data to be used for target identification, sensor data may be collected in response to a trigger event indicative of the laser pulses being delivered to the anatomical target, which typically occurs when the field of view is clear and the treatment device (e.g., a laser fiber) is properly positioned over the anatomical target. An exemplary surgical laser system includes a laser system to emit laser pulses via a laser fiber to an anatomical target in an fluid surgical site, one or more sensors to sense information about properties of the anatomical target, and a controller circuit. The controller circuit can initiate triggered collection of sensor data in a presence of a trigger event indicative of the laser pulses being delivered to the anatomical target, and identify a type or composition of the anatomical target using the triggered collection of sensor data. A laser output setting of the laser system may be determined or adjusted based at least in part on the identified type or composition of the anatomical target.

[0006] Example 1 is a surgical laser system. The system includes: a laser system configured to emit laser pulses via a laser fiber to an anatomical target in a fluid surgical site of a patient; one or more sensors configured to sense information about properties of the anatomical target; and a controller circuit, comprising a feedback analyzer circuit configured to: initiate triggered collection of sensor data in a presence of a trigger event indicative of the laser pulses being delivered to the anatomical target; and identify a type or composition of the anatomical target using the triggered collection of sensor data, wherein the controller circuit is configured to determine or adjust a laser output setting of the laser system based at least in part on the identified type or composition of the anatomical target.

[0007] In Example 2, the subject matter of Example 1 optionally includes a memory circuit configured to continuously record and buffer sensordata sensed by the one or more sensors, wherein the feedback analyzer circuit is configured to, in the presence of the trigger event indicative of the laser pulses being delivered to the anatomical target: set a flag or timestamp of the trigger event on the continuously recorded and buffered sensor data; and identify the type or composition of the anatomical target using a portion of the continuously recorded and buffered sensor data with a specific duration relative in time to the set flag or timestamp and prior to the laser pulses being incident on the anatomical target.

[0008] In Example 3, the subject matter of Example 2 optionally includes the portion of the continuously recorded and buffered sensor data used for identifying the type or composition of the anatomical target that begins at a specific time prior to the set flag or timestamp.

[0009] In Example 4, the subject matter of any one or more of Examples 2-3 optionally includes the portion of the continuously recorded and buffered sensor data used for identifying the type or composition of the anatomical target that begins at the set flag or timestamp.

[0010] In Example 5, the subject matter of any one or more of Examples 2-4 optionally includes the memory circuit that can include a ring buffer with a fixed buffer size, the ring buffer configured to continuously record and buffer the sensor data in accordance with a first-in first-out (FIFO) process, wherein the feedback analyzer circuit is configured to, in the presence of the trigger event, retrieve from the ring buffer a portion of the sensor data recorded therein prior to the set flag or timestamp.

[0011] In Example 6, the subject matter of any one or more of Examples 1-5 optionally includes a light source configured to emit an electromagnetic radiation to the anatomical target, wherein the one or more sensors include a spectroscopic sensor configured to sense spectroscopic data from the anatomical target in response to the electromagnetic radiation at the anatomical target, wherein the feedback analyzer circuit is configured to initiate triggered collection of spectroscopic data in the presence of the trigger event.

[0012] In Example 7, the subject matter of any one or more of Examples 1-6 optionally includes a laser actuator operable by a user to manually activate the laser system to emit the laser pulses, wherein the trigger event includes amanual activation of the laser system to emit the laser pulses to the anatomical target.

[0013] In Example 8, the subject matter of Example 7 optionally includes the laser actuator that can include a foot pedal configured to be pressed to activate the laser system to emit the laser pulses.

[0014] In Example 9, the subject matter of any one or more of Examples 1-8 optionally includes a laser detector that can be configured to detect an emission of laser pulses, wherein the controller circuit is configured to initiate the triggered collection of sensor data in response to the detected emission of laser pulses.

[0015] In Example 10, the subject matter of Example 9 optionally includes the laser detector that can be configured to detect an emission of laser pulses at a specific wavelength, wherein the controller circuit is configured to initiate the triggered collection of sensor data in response to the detected emission of laser pulses at the specific wavelength.

[0016] In Example 11, the subject matter of Example 10 optionally includes, wherein the specific wavelength is in a range of 800 - 1 lOOnm.

[0017] In Example 12, the subject matter of any one or more of Examples 1-11 optionally includes a bubble detector that can be configured to detect a formation of vapor bubbles in the fluid surgical site consequent to the laser pulses being incident on the anatomical target, wherein the controller circuit is configured to initiate the triggered collection of sensor data in response to the detected formation of vapor bubbles in the fluid surgical site.

[0018] In Example 13, the subject matter of Example 12 optionally includes the bubble detector that can be configured to detect the formation of vapor bubbles using images or video frames of the anatomical target and the fluid surgical site obtained by an imaging device of the surgical laser system.

[0019] In Example 14, the subject matter of Example 13 optionally includes the bubble detector that can be configured to detect the formation of vapor bubbles using an acoustic feedback signal traveling across at least a portion of the fluid surgical site and sensed by an acoustic sensor of the surgical laser system.

[0020] In Example 15, the subject matter of any one or more of Examples 1-14 optionally includes a motion detector that can be configured to detect a motion or displacement of the anatomical target consequent to an emission of laser pulses to the anatomical target, wherein the controller circuit is configured to initiate the triggered collection of sensor data in response to the detected motion or displacement of the anatomical target.

[0021] In Example 16, the subject matter of Example 15 optionally includes the motion detector that can be configured to detect the motion or displacement of the anatomical target using images or video frames of the anatomical target obtained by an imaging device of the surgical laser system.

[0022] In Example 17, the subject matter of any one or more of Examples 1-16 optionally includes, wherein to identify the type or composition of the anatomical target includes to identify a calculi target with one or more compositions, wherein the controller circuit is configured to determine or adjust the laser output setting of the laser system based at least in part on the identified calculi target, and to control the laser system to emit the laser pulses to the calculi target under the adjusted laser output setting to ablate or fragment the calculi target.

[0023] In Example 18, the subject matter of any one or more of Examples 1-17 optionally include, wherein to identify the type or composition of the anatomical target includes to identify the anatomical target as a treatment target or a non-treatment target, wherein the controller circuit is configured to control the laser system to enable emission of laser pulses to the treatment target, and to disable emission of laser pulses to the non-treatment target.

[0024] Example 19 is a method of identifying an anatomical target in a fluid surgical site of a patient and providing laser treatment thereof via a laser system. The method includes steps of: sensing information about properties of the anatomical target using one or more sensors; initiating triggered collection of sensor data in a presence of a trigger event indicative of laser pulses being delivered to the anatomical target; identifying a type or composition of the anatomical target using the triggered collection of sensor data; adjusting a laser output setting of the laser system based at least in part on the identified type or composition of the anatomical target; and providing laser pulses to theanatomical target via a laser system in accordance with the adjusted laser output setting.

[0025] In Example 20, the subject matter of Example 19 optionally includes: continuously recording and buffering sensor data sensed by the one or more sensors in a memory circuit; and in the presence of the trigger event indicative of the laser pulses being delivered to the anatomical target, setting a flag or timestamp of the trigger event on the continuously recorded and buffered sensor data; wherein identifying the type or composition of the anatomical target includes using a portion of the continuously recorded and buffered sensor data with a specific duration relative in time to the set flag or timestamp and prior to the laser pulses being incident on the anatomical target.

[0026] In Example 21, the subject matter of Example 20 optionally includes, wherein the portion of the continuously recorded and buffered sensor data used for identifying the type or composition of the anatomical target begins at a specific time prior to the set flag or timestamp.

[0027] In Example 22, the subject matter of any one or more of Examples 20-21 optionally includes, wherein the portion of the continuously recorded and buffered sensor data used for identifying the type or composition of the anatomical target begins at the set flag or timestamp.

[0028] In Example 23, the subject matter of any one or more of Examples 20-22 optionally includes continuously recording and buffering the sensor data in accordance with a first-in first-out (FIFO) process in a ring buffer with a fixed buffer size, wherein the portion of the continuously recorded and buffered sensor data used for identifying the type or composition of the anatomical target occurs prior to the set flag or timestamp.

[0029] In Example 24, the subject matter of any one or more of Examples 19-23 optionally includes sensing the information about properties of the anatomical target that can include sensing spectroscopic data from the anatomical target via a spectroscopic sensor in response to electromagnetic radiation at the anatomical target, wherein the triggered collection of sensor data includes triggered collection of spectroscopic data in the presence of the trigger event.

[0030] In Example 25, the subject matter of any one or more of Examples 19-24 optionally include, wherein the trigger event includes a manual activation of the laser system via a laser actuator to emit the laser pulses to the anatomical target.

[0031] In Example 26, the subject matter of any one or more of Examples 19-25 optionally includes detecting emission of laser pulses at a specific wavelength, wherein the trigger event includes the detected emission of the laser pulses at the specific wavelength.

[0032] In Example 27, the subject matter of any one or more of Examples 19-26 optionally includes detecting a formation of vapor bubbles in the fluid surgical site consequent to the laser pulses incident on the anatomical target based on images or video frames of, or acoustic feedback signal from, the anatomical target, wherein the trigger event includes the detected formation of vapor bubbles in the fluid surgical site.

[0033] In Example 28, the subject matter of any one or more of Examples 19-27 optionally includes detecting a motion or displacement of the anatomical target consequent to emission of laser pulses to the anatomical target, wherein the trigger event includes the detected motion or displacement of the anatomical target.

[0034] In Example 29, the subject matter of any one or more of Examples 19-28 optionally includes identifying a calculi target with one or more compositions, wherein adjusting a laser output setting is based at least in part on the identified calculi target.

[0035] In Example 30, the subject matter of any one or more of Examples 19-29 optionally include identifying the type or composition of the anatomical target that can include identifying the anatomical target as a treatment target or a non-treatment target, wherein adjusting a laser output setting includes enabling emission of laser pulses to the treatment target and disabling emission of the laser pulses to the non-treatment target.

[0036] This summary is an overview of some of the teachings of the present application and not intended to be an exclusive or exhaustive treatment of the present subject matter. Further details about the present subject matter are found in the detailed description and appended claims. Other aspects of thedisclosure will be apparent to persons skilled in the art upon reading and understanding the following detailed description and viewing the drawings that form a part thereof, each of which are not to be taken in a limiting sense. The scope of the present disclosure is defined by the appended claims and their legal equivalents.BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Various embodiments are illustrated by way of example in the figures of the accompanying drawings. Such embodiments are demonstrative and not intended to be exhaustive or exclusive embodiments of the present subject matter.

[0038] FIG. l is a block diagram illustrating an example of a laser treatment system configured to provide laser therapy to a target structure in a body, such as an anatomical structure or a calculus structure.

[0039] FIG. 2 is a block diagram illustrating a surgical laser system and a part of the environment in which said system may be used.

[0040] FIG. 3 illustrates an example of an endoscopic laser lithotripsy system with a feedback control using triggered collection of sensor data.

[0041] FIG. 4 illustrates examples of trigger events to trigger sensor data collection, or to flag a reference timing of a portion of continuously recorded and buffered sensor data that can be used for target identification.

[0042] FIG. 5 illustrates an exemplary computer-based clinical decision support system (CDSS) that is configured to determine a proper laser output setting to be used during the procedure based on features of the identified type or composition of the target structure.

[0043] FIG. 6 a flowchart illustrating an example method for identifying an anatomical target using triggered collection of sensor data, and providing a laser treatment thereto.

[0044] FIG. 7 is a flowchart illustrating an example method for identifying an anatomical target using a selected portion of event-triggered sensor data.

[0045] FIG. 8 is a block diagram illustrating an example machine upon which any one or more of the techniques (e.g., methodologies) discussed herein may perform.DETAILED DESCRIPTION

[0046] Laser endoscopy is a medical procedure of viewing and operating on an internal organ, and delivering surgical laser to a target body region to achieve a particular diagnostic or therapeutic effect. Laser endoscopy have been used for treatment of soft and hard tissue (e.g., damaging or destroying cancer cells), or in lithotripsy applications. For example, in PCNL, a practitioner can insert a rigid scope through an incision in a patient’s back and into the patient’s kidney. Through the scope, the practitioner can locate certain stones in the kidney or upper ureter, break the stones into smaller fragments by illuminating the stone, through the scope, with relatively high-powered infrared laser beam. The laser beam can ablate a stone into smaller fragments. The stone fragments can then be withdrawn from the kidney. The scope can include an endoscope, a nephroscope, and / or a cystoscope.

[0047] In endoscopic laser therapy, it is desirable to recognize different tissue, apply laser energy only to target treatment structures (e.g., cancerous tissue, or a particular calculus type), and avoid or reduce exposing non-treatment tissue (e.g., normal tissue) to laser irradiation. Conventionally, the recognition of a target treatment structure of interest is performed manually by an operator, such as by visualizing the target surgical site and its surrounding environment through an endoscope. At least due to a tight access to an operation site that offers a limited surgical view, manual approach may lack accuracy in some cases, and do not offer in vivo near real-time recognition of target type and / or composition. In vivo near real-time target identification (e.g., target type and composition) is generally desired to reduce surgery time and complexity, and to improve therapy efficacy. For example, in laser lithotripsy that applies laser to break apart or dust a calculi structure, in vivo identification of a calculi structure (e.g., chemical composition of a kidney or pancreobiliary or gallbladder stone) and distinguishing it from surrounding tissue allows more timely adjustment of a laser irradiation setting (e.g., power, exposure time, or firing angle) to ablate thecalculi structure more effectively while avoiding inadvertent irradiation of nontreatment tissue.

[0048] It is also desirable to be capable of continuously monitoring a target and identifying a target type or composition. There are many moving parts during an endoscopic procedure, and the tissue viewed at from the endoscope may change throughout the procedure. Continuous monitoring and recognition of structure type (e.g., soft or hard tissue type, normal tissue versus cancerous tissue, or composition of calculi structures) at the tip of the endoscope may give physicians more information to better adapt the treatment during the procedure. For example, if a physician is dusting a renal calculi that has a hard surface, but a soft core, continuous tissue composition information through the endoscope can allow the physician to adjust the laser setting based on the continuously detected stone surface composition, such as from a first setting that perform better on the hard surface of the stone to a second different setting that perform better on the soft core of the stone.

[0049] One technical challenge for in vivo target identification is to determine an optimal time to collect sensor data (e.g., spectroscopic data, imaging data, or acoustic sensor data) for use in target identification. The clinical environment during a surgery is usually dynamic, which may affect the quality of the sensor data and hence the accuracy and reliability of target identification. For example, to obtain high quality sensor data, it is generally required that the field of view of the target structure is clear, and that the laser fiber tip (where one or more sensors are typically located) be positioned proximal to the target. If sensor data are collected when the laser fiber is in a sub-optimal position, or when the field of view is not clear, then the target identification result may not be accurate. On the other hand, immediately after a laser therapy, the anatomical environment of the target structure may become significantly turbulent. Sensor data collected during this time may introduce artifacts and affect target identification accuracy and reliability. For at least these reasons, the present inventors have recognized an unmet need for apparatus and methods for triggered collection of sensor data, or flagging on continuously collected and buffered sensor data a reference timing of the data portion to be used for in vivotarget identification, such as identifying a type or composition of a calculi target, distinguishing between a calculi target and a tissue, or any other decisions.

[0050] Described herein are systems, devices, and methods for triggered data collection and in vivo target identification in an endoscopy procedure. An exemplary surgical laser system includes a laser system to emit laser pulses via a laser fiber to an anatomical target, one or more sensors to sense information from the anatomical target, and a controller circuit to initiate triggered collection of sensor data in a presence of a trigger event indicative of the laser pulses being delivered to the anatomical target, which generally occurs when the field of view is clear and the laser fiber is properly positioned over the anatomical target. The controller circuit identifies a type or composition of the anatomical target using the triggered collection of sensor data, and adjusts a laser output setting of the laser system based on the result of target identification.

[0051] The systems, devices, and methods according to various embodiments discussed herein provide improved in vivo target identification during the laser procedure. Features described herein may be used in regard to an endoscope, laser surgery, laser lithotripsy, laser settings, and / or spectroscopy. Examples of targets and applications may include laser lithotripsy of renal calculi and laser incision or vaporization of soft tissue. In an example of endoscopic system that incorporate the features as described herein, tissue or calculi types or composition may be identified and monitored in vivo. Automatic and in vivo identification of tissue types or calculi types such as chemical composition of a target may be used to adjust laser settings for optimum delivery of laser energy. The capability of continuous monitoring and identification of tissue types or calculi types allow for the instant adjustment of laser settings. In accordance with various embodiments, an optimal time to initiate triggered sensor data collection or to set a flag or timestamp on continuously collected and buffered sensor data for in vivo target identification, may be automatically determined based on an indication of emission of the laser pulses to the target. The triggered data collection as described herein may improve the quality of the sensor data and accuracy and efficiency of target identification. Accordingly, therapy efficacy and procedure success rate can be increased, incidents ofaccidental laser firing or misplaced laser firing can be reduced, and the tissue safety can be enhanced.

[0052] FIG. l is a block diagram illustrating an example of a laser treatment system 100 configured to provide laser treatment to a target structure 122 in a body of a subject, such as anatomical structure (e.g., soft tissue, hard tissue, or abnormal such as cancerous tissue) or calculus structure (e.g., kidney or pancreobiliary or gallbladder stone). The laser treatment system 100 may include a laser feedback control system 101 and at least one laser system 102. The laser feedback control system 101 may be configured to receive a signal from the target in response to electromagnetic radiation produced by a light source, generate one or more spectroscopic properties using the reflected signal from the target, identify the target as one of a plurality of structure types with respective distinct compositions (e.g., a calculus type or an tissue type), and determine an operating mode of the laser system based on the identified structure type. The laser feedback control system 101 may be used in various applications, such as industrial and / or medical applications for treatment of soft (e.g., noncalcified) or hard (e.g., calcified) tissue, or calculi structures such as kidney or pancreobiliary or gallbladder stones. In some examples, the laser treatment system 100 may deliver precisely controlled therapeutic treatment of tissue or other anatomical structures (e.g., tissue ablation, coagulation, vaporization, or the like) or treatment of non-anatomical structures (e.g., ablation or dusting of calculi structures).

[0053] The laser feedback control system 101 may be in operative communication with one or more laser systems. FIG. 1 shows the laser feedback system connected to a first laser system 102 and optionally (shown in dotted lines) to a second laser system 104. Additional laser systems are contemplated within the scope of the present disclosure. The first laser system 102 may include a first laser source 106, and associated components such as power supply, display, cooling systems and the like. The first laser system 102 may also include a first optical pathway 108 operatively coupled with the first laser source 106. In an example, the first optical pathway 108 includes an optical fiber. The first optical pathway 108 may be configured to transmit laser beams from the first laser source 106 to the target structure 122.

[0054] The laser feedback control system 101 may analyze feedback signals 130 from the target structure 122, and control the first laser system 102 and / or the second laser system 104 to generate suitable laser outputs for providing a desired therapeutic effect. For instance, the laser feedback control system 101 may monitor properties of the target structure 122 during a therapeutic procedure (e.g., ablating calculi such as kidney stones into smaller fragments) to determine if the tissue was suitably ablated prior to another therapeutic procedure (e.g., coagulation of blood vessels).

[0055] In an example, the first laser source 106 may be configured to provide a first output 110. The first output 110 may extend over a first wavelength range, such as one that corresponds to a portion of the absorption spectrum of the target structure 122. The first output 110 may provide effective ablation and / or carbonation of the target structure 122 since the first output 110 is over a wavelength range that corresponds to the absorption spectrum of the tissue.

[0056] In an example, the first laser source 106 may be configured such that the first output 110 emitted at the first wavelength range corresponds to high absorption (e.g., exceeding about 250 cm’1) of the incident first output 110 by the tissue. In example aspects, the first laser source 106 may emit first output 110 between about 1900 nanometers (nm) and about 3000 nm (e.g., corresponding to high absorption by water) and / or between about 400 nm and about 520 nm (e.g., corresponding to high absorption by oxy- hemoglobin and / or deoxy-hemoglobin). Appreciably, there are two main mechanisms of light interaction with a tissue: absorption and scattering. When the absorption of a tissue is high (absorption coefficient exceeding 250 cm’1) the first absorption mechanism dominates, and when the absorption is low (absorption coefficient less than 250 cm’1), for example lasers at 800-1100 nm wavelength range, the scattering mechanism dominates.

[0057] Various commercially available medical-grade laser systems may be suitable for the first laser source 106. For instance, semiconductor lasers such as InXGal-XN semiconductor lasers providing the first output 110 in the first wavelength range of about 515 nm and about 520 nm or between about 370 nmand about 493 nm may be used. Alternatively, infrared (IR) lasers such as those summarized in Table 1 below may be used.Table 1 Example List of suitable IR lasersLaser velength Absorption Coefficient Optical Penetration DepthA. (nm) (cm'1) 8 (pm)Thulium fiber laser: 1908 88 / 150 114 / 67Thulium fiber laser: 1940 120 / 135 83 / 75ThufiimrYAO: 2010 62 / 60 161 / 167Hoitn mYAG: 2120 24 / 24 417 / 417Erbium: YAG: 2940 12.000 / 1,000 1 / 10

[0058] The optional second laser system 104 may include a second laser source 116 for providing a second output 120, and associated components, such as power supply, display, cooling systems and the like. The second laser system 104 may either be operatively separated from or, in the alternative, operatively coupled to the first laser source 106. In some embodiments, the second laser system 104 may include a second optical pathway 118 (separate from the first optical pathway 108) operatively coupled to the second laser source 116 for transmitting the second output 120. Alternatively, the first optical pathway 108 may be configured to transmit both the first output 110 and the second output 120.

[0059] In certain aspects, the second output 120 may extend over a second wavelength range, distinct from the first wavelength range. Accordingly, there may not be any overlap between the first wavelength range and the second wavelength range. Alternatively, the first wavelength range and the second wavelength range may have at least a partial overlap with each other. In advantageous aspects of the present disclosure, the second wavelength range may not correspond to portions of the absorption spectrum of the target structure 122 where incident radiation is strongly absorbed by tissue that has not been previously ablated or carbonized. In some such aspects, the second output 120 may advantageously not ablate uncarbonized tissue. Further, in another embodiment, the second output 120 may ablate carbonized tissue that has been previously ablated. In additional embodiments, the second output 120 may provide additional therapeutic effects. For instance, the second output 120 may be more suitable for coagulating tissue or blood vessels.

[0060] FIG. 2 is a block diagram illustrating a surgical laser system 200, and at least a portion of the environment in which the system 200 may operate.The system 200 can be an embodiment of the laser energy delivery system 100, or a lithotripsy system that may be used for destructing hardened masses like renal stones, bezoars, gallstone, among other calculi structures.

[0061] The surgical laser system 200 may include a feedback control system 210, one or more sensors 220, a laser system 230, a memory circuit 240, and a user interface device 250. The feedback control system 210, which is an embodiment of the feedback control system 101 of the laser energy delivery system 100, may include a data collection / analysis trigger circuit 211, a feedback analyzer 212, and a controller circuit 214. According to example embodiments, the feedback control system 210 may include processors, such as microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components for performing one or more of the functions attributed to the feedback control system 210.

[0062] The feedback analyzer 212 may be communicatively coupled to one or more sensors 220, receive therefrom feedback signals, and analyze the feedback signals to generate one or more signal metrics that may be used for target detection, localization, and / or identification, which may further be used for determining or adjusting laser output. By way of non-limiting examples and as illustrated in FIG. 2, the one or more sensors 220 may include one or more of a spectroscopic sensor 222, an imaging sensor 224, or an acoustic sensor 226. The spectroscopic sensor 222 may sense a spectroscopic signal from the target structure 122, and generate one or more spectroscopic properties, such as reflectivity, reflectance spectrum, absorption index, among others. Examples of the spectroscopic sensor 222 may include a Fourier Transform Infrared (FTIR) spectrometer, a Raman spectrometer, a UV-VIS spectrometer, a UV-VIS-IR spectrometer, or a fluorescent spectrometer, among others. Each spectroscopic sensor 222 may correspond to a spectroscopy technique. For example, UV-VIS reflection spectroscopy may be used to gather information from the light reflected off an object similar to the information yielded from the eye or a color image made by a high resolution camera, but more quantitatively and objectively. The reflection spectroscopy may offer information about thematerial since light reflection and absorption depends on its chemical composition and surface properties. Information about both surface and bulk properties of the sample may be obtained using this technique. The reflection spectroscopy may be used to recognize composition of hard or soft tissue. Fluorescent spectroscopy is a type of electromagnetic spectroscopy that analyzes fluorescence from a sample. It involves using a beam of light, usually ultraviolet, that excites a material compound and causes the material compound to emit light, typically in visible or IR area. The method may be applied for analysis of some organic components such as hard and soft tissue. FTIR spectroscopy may be used for rapid materials analysis, and has relatively good spatial resolution and gives information about the chemical composition of the material. Raman spectroscopy may be used for identifying hard and soft tissue components. As a high spatial resolution technique, it is also useful for determining distribution of components within a target. The spectroscopy techniques as described above may be used alone or in combination to analyze the spectroscopic signal by the spectroscopic sensor 222 to generate one or more spectroscopic properties indicative of structure types with respective distinct compositions.

[0063] The imaging sensor 224 can generate images or video frames of at least a portion of the target structure 122 during the endoscopic procedure. The imaging sensor 224 may be included in an imaging system that further includes a lens system. The imaging sensor 224 may take the form of an imaging camera, such as a CCD or CMOS camera sensitive in ultraviolet (UV), visible (VIS) or infrared (IR) wavelengths in an embodiment. In some embodiments, the spectroscopic sensor 222 may include more than a single type of spectrometer or imaging camera listed herein to enhance sensing and detection of various features (e.g., carbonized and non-carbonized tissue, vasculature, and the like). The imaging sensor 224 may be located a distal portion of an endoscope for use during the procedure, an example of which is illustrated in FIG. 3. The imaging sensor 224 may generate images or video frames at different times.

[0064] The acoustic sensor 226 can sense an acoustic signal in response to delivery of an excitation signal (e.g., laser pulses) incident on the target structure 122. The acoustic signal may be sensed when a laser pulse propagates through liquid media along the path to the target structure 122 and cause theliquid to vibrate. In some examples, the acoustic sensor 226 may sense certain sound waves with specific wavelengths, such as audible range of waves, ultrasonic wave, or infrasonic waves. Examples of the acoustic sensor 226 may include microphones, hydrophones, capacitive sensors, piezoelectric sensor, piezoceramic sensor, fiber-optic sensors, or solid-state acoustic detectors, among others. The feedback analyzer 212 may analyze the acoustic signals to generate one or more acoustic properties. Examples of the acoustic properties may include intensity, power, frequency or spectral content, or a graphical feature representing a shape of the received acoustic signal (e.g., a shape characteristic of a time series of sound intensity). In some examples, the acoustic properties may include one or more statistical features (e.g., signal mean or variance) of the received acoustic signal.

[0065] The feedback analyzer 212 may detect, localize, and identify the target structure 122 using the sensor signals or signal properties from the one or more sensors 220. Because of the dynamic clinical environment during the surgical procedure, it is desirable to determine an optimal time to initiate sensor data collection for in vivo target identification, as high-quality sensor data are important for an accurate and reliable target identification. As illustrated in FIG. 2, the data collection / analysis trigger circuit 211, communicatively coupled to the feedback analyzer 212, can automatically initiate triggered collection of sensor data in the presence of a trigger event indicative of the laser pulses being delivered to the target structure 122. In an example, the sensor data sensed by the one or more sensors 220 may be continuously recorded and buffered in the memory circuit 240. In the presence of a trigger event indicative of the laser pulses being delivered to the target structure 122, the feedback analyzer 212 may set a flag or timestamp on the continuously collected and buffered sensor data. A portion of the recorded sensor data with a specific duration relative in time to the set flag or timestamp and prior to the laser pulses being incident on the anatomical target may be used for in vivo target identification. The portion of the continuously recorded and buffered sensor data used for target identification may begin at the set flag or timestamp in one example, or at a specific time prior to the set flag or timestamp in another example. In an example, the portion of thecontinuously recorded and buffered sensor data used for target identification may occur during a time period right before the set flag or timestamp.

[0066] To ensure efficient storage of the triggered sensor data with respect to the trigger event, in an example, a ring buffer that uses a first-in firstout (FIFO) data storing process may be implemented in the memory circuit 240. A ring buffer (also known as a circular buffer or a circular queue) is a buffer data structure that behaves as if it had a circular shape, in which the last element in the buffer is connected to the first element. The ring buffer typically has a fixed buffer size that allows newly collected sensor data to overwrite at least a part of the previously buffered sensor data in accordance with a FIFO logic. In an example of collecting spectroscopic scans, if the ring buffer has a fixed size that can hold as many as N scans, after the ring buffer is filled in full with scans #1 to #N, when new scans (e.g., #(N+1) and #(N+2)) are collected. Scan #(N+1) can overwrite scan #1, and scan #(N+2) can overwrite scan #2, etc. As such, the ring buffer ensures that at any time, only the most recent N scans are retained in the buffer. The ring buffer may continually fill with newly collected sensor data (e.g., spectroscopic scans) until a trigger event (e.g., any of the events 410, 420, 430, or 440, such as a foot pedal press) is detected, which sets a flag or timestamp in the buffer. A portion of the sensor data stored in the ring buffer with a specific duration relative in time to the set flag or timestamp, such as most recent M scans (M<=N) before the set flag or timestamp, may be retrieved from the ring buffer. The feedback analyzer 212 may use the retrieved sensor data to detect, localize, and identify the target structure 122. Additionally or alternatively, the retrieved sensor data may be used in other applications such as target identification algorithm development. In an example, the retrieved sensor data may be used for constructing a training dataset used for training an artificial intelligence (Al) or machine learning (ML) based prediction model, as will be discussed further later in this document. In some examples, a similar or different buffer may be included in the memory circuit 240 to store post-trigger sensor data (i.e., sensor data collected after the detected trigger event). This may help provide a continuous record of timeline of events throughout the procedure session.

[0067] The trigger event that automatically triggers sensor data collection or flags a reference timing of sensor data portion for target identification may be an event indicative of emission of the laser pulses to the target structure 122, or an event consequent to the emission of the laser pulses. Referring to FIG. 4, a trigger event detector 400, which may be a part of the system 200 such as included in the feedback control system 210, may detect a trigger event that triggers sensor data collection, or sets a flag or timestamp on the continuously recorded and buffered sensor data. By way of example and not limitation, the trigger event may include one or more of a laser actuator activation event 410, a laser emission event 420, a vapor bubble formation event 430, or a detected target motion or displacement 440. As illustrated in FIG. 2, the laser system 230 may be coupled to a laser actuator 232 operable by a user to manually activate, or confirm an automated activation of, the laser system 230 to emit laser pulses to the target structure 122. An example of the laser actuator is a foot pedal that, when pressed by the operator, initiates laser emission to the target structure 122. The laser actuator activation event 410 is a detected activation of a laser actuator 232, such as detected food petal press. When the laser system is set for lithotripsy treatment mode, the foot pedal press indicates a readiness of laser emission, and typically suggests a high confidence that the laser fiber is in close proximity to the target structure 122, and the field of view of the target structure 122 is clear. In the case of continuous sensor data collection and buffering to a memory, the foot pedal press may trigger flagging of the buffered data and providing a portion of the buffered data to the feedback analyzer 212 for target detection, localization, and identification, among other decisions that may affect the safety or efficacy of the laser at treating the patient. In some examples, the laser actuator activation event 410, such as the detected foot pedal press, may also represent an optimal time to save buffered data for use in training algorithms or making decisions since it is most likely to occur when the field of view is clear and the fiber is properly positioned over a target.

[0068] The laser emission event 420 represents an emission of laser pulses detected by a laser detector. The laser detector can be configured to detect laser pulses at a specific wavelength or a range of wavelength, such as approximately 1940 nm in the case of a thulium laser in one example, or in a 800- 1100 nanometers (nm) range in another example. The feedback analyzer 212 may initiate sensor data collection, or flag the continuously collected and buffered data stream, in response to a detection of emission of laser pulses at the specific wavelength or wavelength range.

[0069] The vapor bubble formation event 430 represents a detected formation of vapor bubbles in the anatomical environment of the target structure 122 consequent to the laser pulses incident on the target, which generates heat and cause fluid vibration and promotes formation of bubbles. The detected vapor bubble formation therefore indicates laser emission to the target. The trigger event detector 400 may include a bubble detector that detects the formation of vapor bubbles using sensor information. In an example, the bubble detector may detect bubble formation using images or video frames of the target structure 122 and surrounding anatomical environment, which can be obtained by an imaging device of the system 200. In another example, the bubble detector may detect bubble formation using an acoustic feedback signal traveling across at least a portion of the anatomical environment and sensed by an acoustic sensor of the system 200. The feedback analyzer 212 may initiate sensor data collection, or set a flag or timestamp on the continuously recorded and buffered data stream, in response to the detected formation of vapor bubbles in the anatomical environment.

[0070] The target motion or displacement 440 consequent to the emission of laser pulses to the target is another indicator of laser emission to the target structure 122. The trigger event detector 400 may include a motion detector that detects a motion or displacement of the target structure 122 consequent to the emission of laser pulses to the target. In an example, the motion detector may detect motion or displacement of the target using images or video frames of the target structure 122 and surrounding anatomical environment, which can be obtained by an imaging device of the system 200. In another example, the motion detector may detect motion or displacement of the target using an acoustic feedback signal sensed by an acoustic sensor of the system 200. The feedback analyzer 212 may initiate sensor data collection, or set a flag or timestamp on the continuously recorded and buffered data stream, in response to the detected motion or displacement of the target.

[0071] Referring back to FIG. 2, the feedback analyzer 212 may use the images or video frames of the target to determine the location of the target structure 122. In some examples, the feedback analyzer 212 may use one or more spectroscopic, imaging, or acoustic properties to identify the target structure 122 as one of a plurality of structure categories, such as a category of calculi structure, or a category of anatomical structure. The signal metrics may include intensity, power, frequency or spectral content, a graphical feature or shape, or one or more statistical features of the received spectroscopic sensor signal, imaging sensor signal, or acoustic signal. For a tissue target or a calculi target, its ability to absorb laser energy depends on its composition and liquid content. Different target types, such as different calculi structures or soft or hard tissue, may have different composition and / or liquid content. When these targets absorb different amount of laser energy, they may produce respective different spectroscopic properties and / or acoustic properties. Examples of calculi structure may include stones or stone fragments in various stone-forming regions such as urinary system, gallbladder, nasal passages, gastrointestinal tract, stomach, or tonsils. Examples of the anatomical structure may include soft tissue (e.g., muscles, tendons, ligaments, blood vessels, fascia, skin, fat, and fibrous tissues), hard tissue such as bone, connective tissue such as cartilage, among others.

[0072] In an example, the feedback analyzer 212 may identify the target structure 122 as one of a plurality of structure types of the same category, such as a particular tissue type within an identified category of anatomical structure, or as a particular calculi type within an identified category of calculi structure. In an example, the feedback analyzer 212 may identify a calculi structure as one of stone types with distinct chemical compositions, such as one of a CaP stone, a MAP stone, a COM stone, a COD stone, a cholesterol-based stone, a cystine stone, or a uric acid (UA) stone. The target identification may be made based on one or more of spectroscopic properties or acoustic properties, such as intensity, power, frequency or spectral content, or a graphical feature or shape of the received spectroscopic or acoustic signal, or one or more statistical features generated from the received signal. In some example, the feedback analyzer 212 may identify an identified anatomical structure as one of plurality of tissue types. The tissue types may include tissue at distinct anatomical locations, such ascalyx tissue, cortex tissue, medulla tissue, ureter tissue, or bladder tissue. In another example, the feedback analyzer 212 may identify an anatomical structure as normal tissue or abnormal tissue (e.g., cancerous tissue). In another example, the feedback analyzer 212 may identify an anatomical structure as a treatment area (e.g., tumor or polyp intended for removal) or a non-treatment area (e.g., blood vessels, muscle, etc.).

[0073] The controller circuit 214 may be coupled by wired or wireless connections to the feedback analyzer 212. The controller circuit 214 may adjust a laser output setting of the laser system 230 based at least in part on the identified type or composition of the target structure 122. The laser system 230, which is an example of the laser system 102 or the laser system 104 as shown in FIG. 1, can include a laser source (such as the first laser source 106) and an optical pathway (such as the first optical pathway 108) for directing the laser energy to the surgical site. The laser source can generate laser energy in accordance with a laser output intensity or one or more laser irradiation parameters (e.g., one or more laser pulse parameters such as, power, duration, frequency, or pulse shape, exposure time, or firing angle). At least some of such laser parameters are programmable or adjustable either automatically such as by the controller circuit 214, or manually by a user via the user interface device 250.

[0074] In various examples, the controller circuit 214 may adjust the operation of the system (such as a laser output setting) using artificial intelligence (Al) or machine learning (ML) based techniques. For example, information about the identified type or composition of the target structure may be applied to a trained ML model to automatically determine a proper laser setting to be used during the procedure. In some examples, sensor data collected from the one or more sensors 220 in response to a trigger event, or a selected portion of continuously recorded and buffered sensor data with a specific duration that begins at a time relative to a trigger event (e.g., at or right before the trigger event) and ends prior to the laser pulses being incident on the anatomical target, may be applied directly to a trained ML model that outputs a proper laser setting. The ML model may be trained using sensor data from multiple patients that are collected or flagged in response to similar triggerevents. Examples of using a trained ML model to determine a laser output setting are discussed below with respect to FIG. 5.

[0075] The laser system 230 may be coupled to a laser actuator 232 operable by a user to manually activate (or confirm an automated activation of) the laser system 230 to emit laser pulses to the target structure. An example of the laser actuator is a foot pedal that, when pressed by the operator, activates laser emission to the target structure.

[0076] In some examples, the laser system 230 may be associated with one of two distinct operating modes or states: a first state wherein the laser system 230 generates a laser output, and a second state where a laser system 230 does not generate a laser output. For instance, the first laser system 102 may have a first state where a first output 110 (e.g., over the first wavelength range) is generated, and a second state where the first output 110 is not generated. Similarly, the second laser system 104 may have a first state where a second output 120 (e.g., over the second wavelength range) is generated, and a second state where the second output 120 is not generated. In such embodiments, the controller circuit 214 may control the laser system 230 by sending control signals that change the operating state the laser system from the first state to the second state, or from the second state to the first state. In some examples, the laser system 230 may have additional states, for instance, a third state where a laser output in accordance with a different laser irradiation parameter setting is generated. Accordingly, additional control signals may be sent by the controller circuit 214 to the laser system(s) to change their states from their current state to one or more additional states (e.g., first state to third state, second state to third state, third state to first state, and third state to second state) to generate laser outputs that provide a desired therapeutic effect.

[0077] In an example, the controller circuit 214 may generate a control signal to operate the laser system 230 in a first operating mode if the target is identified as a calculi structure, or a second operating mode if the target is identified as an anatomical structure, or a third operating mode if the target is identified as neither an anatomical structure nor a calculi structure. In an example, the first operating mode may include activating the laser system 230 to deliver a laser beam programmed with a first irradiation parameter setting toablate or dust the identified calculi, such as renal stones. In an example, the second operating mode may include withholding laser delivery, or delivering a laser beam programmed with a second irradiation parameter setting different from the first irradiation parameter setting to an identified tissue. In an example, the third operating mode may include deactivating the laser system 230 from delivery of laser energy. The laser irradiation parameters may include wavelength, power, power density, pulse parameters (e.g., pulse width, pulse rate, amplitude, duty cycle), exposure time, total dose or energy, among others.

[0078] In some examples, the controller circuit 214 may determine the operating mode of the laser system 230 based on an identification of the target structure 122 as one of a plurality of calculi types, such as CaP stone, a MAP stone, a COM stone, a COD stone, a cholesterol-based stone, a cystine stone, or a uric acid (UA) stone, as determined by the feedback analyzer 212. The controller circuit 214 may adjust the irradiation parameter setting based on the identification of calculi type, and generate a control signal to control the laser system 230 to deliver laser energy to the target structure 122 in accordance with the adjusted irradiation parameter setting.

[0079] In some examples, the controller circuit 214 may determine the operating mode of the laser system 230 based on the identification of the target structure 122 as one of a plurality of tissue types, such as renal tissue at different anatomical locations (e.g., calyx tissue, cortex tissue, medulla tissue, ureter tissue, or bladder tissue), normal or abnormal tissue (e.g., cancerous tissue), treatment area (e.g., tumor or polyp intended for removal) or a non-treatment area (e.g., blood vessels, muscle, etc.). The controller circuit 214 may adjust the irradiation parameter setting based on the identification of tissue type, and generate a control signal to the laser system 230 that delivers laser energy to the identified anatomical structure in accordance with the adjusted irradiation parameter setting.

[0080] In some examples, the controller circuit 214 may adjust the irradiation parameter setting directly based on one or more of the spectroscopic properties or acoustic properties produced by the feedback analyzer 240 without using information about target type or target composition such as generated by the feedback analyzer 212. For example, the intensity of the feedback signalproduced in response to laser firing at a target calculi structure is correlated to laser power density. The controller circuit 214 may automatically adjust an irradiation parameter setting (e.g., laser power) and laser fiber position to achieve a desired feedback signal amplitude.

[0081] In various examples, the feedback analyzer 212 may continuously monitor the target structure 122, collect and analyze feedback signals, and continuously communicate with the controller circuit 214. Accordingly, the controller circuit 214 may continue maintaining the laser systems in one or more states until a change in the feedback is detected (e.g., a different category of the target structure 122, a different tissue type, or a different calculi type). When a change in feedback is detected, the controller circuit 214 may communicate with the one or more laser systems and change their state(s) to deliver a desired therapeutic effect. Alternatively or additionally, the controller circuit 214 may communicate with an operator (e.g., healthcare professional), and display one or more output(s) via one or more output system(s) indicative of the feedback signal, and may, optionally, instruct the operator to perform one or more treatment procedures with the first laser system and / or the second laser system to deliver a desired therapeutic effect.

[0082] In illustrative examples described herein, the controller circuit 214 may control more than one laser system by changing the operating state of each laser system. According to an aspect, the controller circuit 214 may independently control each laser system. For instance, the controller circuit 214 may send a distinct control signal to each laser system to control each laser system independently of the other laser systems. Alternatively, the controller circuit 214 may send a common signal to control one or more laser systems.

[0083] The user interface device 250 may be operatively in communication with the feedback control system 210. The user interface device 250 may include an output / display unit 252 to display information including, for example, feedback signals sensed by the one or more sensors 220, target detection, localization, and identification results generated by the feedback analyzer 212, or current device settings such as laser irradiation parameters. The output / di splay unit 252 may display UI elements including visual elements, alerts, tactile feedback, or any combination thereof. The output / di splay unit 252may generate an alert or notification to the user about detection of a trigger event that triggers sensor data collection, or sets a flag or timestamp on the continuously recorded and buffered sensor data. The alert may be presented in an audible, visible, tactile, or otherwise human-perceptible format.

[0084] The user interface device 250 may include one or more input units 254 to receive user programming of the device, such as parameter values used for analyzing sensor signals and detecting target location and identifying target types. The user input may include adjustment of laser irradiation parameters or other device parameters.

[0085] FIG. 3 illustrates an example of an endoscopic laser lithotripsy system 300 with a feedback control using triggered collection of sensor data, which can be an example of the surgical laser system 200. The endoscopic laser lithotripsy system 300 may include an endoscope 301, a feedback control system 310, and an actuator 338. The endoscope 301 has a proximal portion and an elongate distal portion that may be configured to be inserted into a surgical site of a patient during an endoscopic laser lithotripsy procedure. The endoscope 301 may provide visual inspection or treatment of soft (e.g., non-calcified) or hard (e.g., calcified) tissue as well as for visualizing or breaking up or otherwise treating renal stones or other calculi structures or targets.

[0086] As illustrated in FIG. 3, the endoscope 301 may include or provide visualization and illumination optics, such as a visualization optical pathway 360 and an illumination optical pathway 350, each of which may extend longitudinally along the elongate body of the endoscope 301. An eyepiece or camera or imaging display may be provided at or coupled to the visualization optical pathway 360 to permit user or machine visualization of a target region at or near a distal end of the endoscope 301. The target region may be illuminated by light 370, such as provided by an illumination light source 324 at a proximal end of the illumination optical pathway 350 and emitted from a distal end of the illumination optical pathway 350. The light source 324 can include, for example, a Xenon lamp, a light-emitting diode (LED), a laser diode (LD), or any combination thereof. In an example, the light source 324 may include two or more light sources that emit light having different illumination characteristics, referred to as illumination modes. In an example, theillumination modes may include a white light illumination mode, or a special light illumination mode such as a narrow band imaging mode, an auto fluorescence imaging mode or an infrared imaging mode. A special light illumination can concentrate and intensify specific wavelengths of light, for example, resulting in a better visualization of tissue or other structures at the surgical site.

[0087] The lithotripsy system 300 may include or be coupled to a laser source 332, which may be an example of the first laser source 106, the second laser source 116, or the laser system 230. The laser source 332, which may be included in the laser system 230, may be mechanically and optically connected to an optical pathway 334, which may include a single optical fiber or a bundle of optical fibers. The optical pathway 334, which is an embodiment of the first optical pathway 108 or the second optical pathway 118, or the optical pathway included in the laser system 230, may be introduced via a proximal access port to extend within a working channel or other longitudinal passage or lumen of the endoscope 301 or similar instrument.

[0088] In some examples, the laser source 332 may include a first laser source to generate a treatment beam 383 A and a second different laser source to generate an aiming beam 383B. The treatment beam and the aiming beam can be directed to the target through the same or a different optical pathways. In some examples, the aiming beam may be generated using a light source different than the second laser source. The aiming beam may have a distinct color (e.g., green or red) to distinguish from the illumined background of the surgical site.

[0089] The lithotripsy system 300 may include one or more sensors to sense information from the anatomical target or the surgical site, including a spectroscopic sensor 222. As described above with reference to FIG. 2, the spectroscopic sensor 222 may sense a spectroscopic signal from the target structure 122. The spectroscopic sensor 222 may be located at a distal end 336 of the optical pathway 334. The feedback control system 310 includes a feedback analyzer 312 and a controller circuit 314. The feedback analyzer 312, which is an embodiment of the feedback analyzer 212, may include a spectrometer that generates one or more spectroscopic properties from a spectroscopic sensor signal, or one or more acoustic properties from an acoustic signal, as describedabove with respect to the feedback analyzer 212. The feedback analyzer 312 may detect, localize, and identify the target structure 122 using the spectroscopic signals or imaging signals. In an example, the feedback analyzer 312 may additionally recognize the target as a calculi target or anatomical target at or near the surgical site, or classify the target as one type of tissue or one type of calculi of distinct composition using the one or more spectroscopic properties. In some examples, the feedback analyzer 312 may calculate or estimate the fiber-target distance using the spectroscopic properties. The controller circuit 314 may generate a control signal to the laser source 332 to adjust a laser output setting, a control signal to the actuator 338 to adjust the position or orientation of the distal end 336 of the optical pathway 334 based on the structure, composition, or type of the target.

[0090] The lithotripsy system 300 may include a camera or imaging device 325. The camera or imaging device 325 can include an imaging sensor (such as the imaging sensor 224) that can generate an imaging signal 365 of the target in response to electromagnetic radiation (e.g., illumination light 370) of the target at or near the surgical site. The imaging signal 365 may be transmitted through the optical pathway 360, or alternatively through the optical pathway 334, to the feedback control system 310 (an embodiment of the feedback control system 210). In an example, the imaging signal 365 may pass through an optical splitter before reaching the feedback analyzer 312. The feedback analyzer 312 may use the imaging signal 365 to determine target location. The feedback analyzer 312 may detect, localize, and identify the target structure 122 using the spectroscopic signals or imaging signal. In an example, the feedback analyzer 312 may additionally recognize the target as a calculi target or anatomical target at or near the surgical site, or classify the target as one type of tissue or one type of calculi of distinct composition using the one or more spectroscopic properties.

[0091] The data collection / analysis trigger circuit 311, communicatively coupled to the feedback analyzer 312, may automatically trigger sensor data collection, or set a flag or timestamp on continuously collected and buffered sensor data stream marking a reference timing of the data stream to be used for in vivo target identification in the presence of a trigger event. The trigger event may be indicative of emission of the laser pulses to the target structure, or anevent consequent to the emission of the laser pulses. Examples of the trigger events are described with respect to FIG. 4.

[0092] In some examples, the feedback analyzer 312 may calculate or estimate the fiber-target distance using the spectroscopic properties. The controller circuit 314 may generate a control signal to the laser source 332 to adjust a laser output setting based on the structure, composition, or type of the target.

[0093] The controller circuit 314 may generate a control signal to the laser source 332 to automatically adjust a laser output setting, including one or more laser irradiation parameters, based at least in part on the target identification (e.g., target location, type, and composition).

[0094] In an example, laser pulses incident on the target structure 122 may produce bubbles 392 of fluid vapor at the vicinity of the target structure 122. A detected vapor bubble formation is an indication of laser emission to the target. Formation of vapor bubbles may be detected using images or video frames of the target structure and surrounding anatomical environment, which can be obtained by an imaging device 325. Alternatively, formation of vapor bubbles may be detected using acoustic feedback signal traveling across at least a portion of the anatomical environment and sensed by an acoustic sensor (not shown). The feedback analyzer 312 may initiate sensor data collection, or set a flag or timestamp on the continuously recorded and buffered data stream, in response to the detected formation of vapor bubbles in the anatomical environment.

[0095] In addition or alternative to adjusting laser output settings, in some examples, the controller circuit 314 may generate a control signal to an actuator 338 to adjust the position of the laser fiber distal end 336 relative to the target structure 122. The actuator 338 may be a laser emitting end coupled to a portion of the optical pathway 334, and can be in electrical communication with the controller circuit 314. In an example, the actuator 338 may be located at or near the distal end of the endoscope 301. The actuator 338 may include one or more of an electromagnetic element, an electrostatic element, a piezoelectric element, or other actuating element such as to actuate or otherwise permit longitudinal or rotational positioning of the laser fiber distal end 336 withrespect to the working channel or other longitudinal passage of the endoscope 301, or with respect to another reference location for which the endoscope 301 may serve as a frame of reference.

[0096] FIG. 5 is a schematic diagram of an exemplary computer-based clinical decision support system (CDSS) 510 that is configured to determine a proper laser output setting to be used during the procedure based on features of the identified type or composition of the target structure (hereinafter the “input features”). In various embodiments, the CDSS 510 includes an input interface 512 through which the input features which are specific to a patient are provided to a trained ML model 514 (also referred to as an Al model) . The controller circuit 214 (shown in FIG. 2) performs an inference operation in which the input features are applied to the ML model 514 to generate a laser output setting as an inference output at the output interface 516. The inference output may be output to an output device 540, which may include a user interface (UI) through which laser output setting can be communicated to a user, e.g., a clinician, or to a controller device such as the controller circuit 214 for performing a desired action.

[0097] In some embodiments, the input interface 512 may be a direct data link between the CDSS 510 and one or more feature generating devices 530 that generate at least some of the input features. For example, the input interface 512 may transmit the input features directly to the CDSS 510 during a therapeutic and / or diagnostic medical procedure. Additionally, or alternatively, the input interface 512 may be a classical user interface that facilitates interaction between a user and the CDSS 510. For example, the input interface 512 may facilitate a user interface through which the user may manually enter at least some of the input features. Additionally, or alternatively, the input interface 512 may provide the CDSS 510 with access to a database of electronic patient record 520 from which one or more input features may be extracted. In any of these cases, the input interface 512 is configured to collect one or more of the image or video frames or features in association with a specific patient on or before a time at which the CDSS 510 is used to determine a proper laser output setting.

[0098] The controller circuit 214 may perform an inference operation using the ML model 514 to generate a proper laser output setting. For example, input interface 512 may deliver the one or more input features into an input layer of the ML model 514 which propagates these input features through the ML model 514 to an output layer. The ML model 514 can provide a computer system the ability to perform tasks, without explicitly being programmed, by making inferences based on patterns found in the analysis of data. The ML model 514 explores the study and construction of algorithms (e.g., machinelearning algorithms) that may learn from existing data and make predictions about new data. Such algorithms operate by building an ML model from example training data in order to make data-driven predictions or decisions expressed as outputs or assessments, such as adjusting the laser output setting and delivering laser pulses to the target.

[0099] The trained ML model is able to perform tasks, without explicitly being programmed, by making inferences based on patterns found in the analysis of data. The ML model explores the study and construction of algorithms (e.g., ML algorithms) that may learn from existing data and make predictions about new data. Such algorithms operate by building the ML model from training data in order to make data-driven predictions or decisions expressed as outputs or assessments.

[0100] The ML model may be trained using supervised learning or unsupervised learning. Supervised learning uses prior knowledge (e.g., examples that correlate inputs to outputs or outcomes) to learn the relationships between the inputs and the outputs. The goal of supervised learning is to learn a function that, given some training data, best approximates the relationship between the training inputs and outputs so that the ML model can implement the same relationships when given inputs to generate the corresponding outputs. Unsupervised learning is the training of an ML algorithm using information that is neither classified nor labeled, and allowing the algorithm to act on that information without guidance. Unsupervised learning is useful in exploratory analysis because it can automatically identify structure in data.

[0101] Common tasks for supervised learning are classification problems and regression problems. Classification problems, also referred to ascategorization problems, aim at classifying items into one of several category values. Regression algorithms aim at quantifying some items (for example, by providing a score to the value of some input). Some examples of commonly used supervised-ML algorithms are Logistic Regression (LR), Naive-Bayes, Random Forest (RF), neural networks (NN), deep neural networks (DNN), matrix factorization, and Support Vector Machines (SVM). Examples of DNN include a convolutional neural network (CNN), a recurrent neural network (RNN), a deep belief network (DBN), or a hybrid neural network comprising two or more neural network models of different types or different model configurations.Some common tasks for unsupervised learning include clustering, representation learning, and density estimation. Some examples of commonly used unsupervised learning algorithms are K-means clustering, principal component analysis, and autoencoders.

[0102] Another type of ML is federated learning (also known as collaborative learning) that trains an algorithm across multiple decentralized devices holding local data, without exchanging the data. This approach stands in contrast to traditional centralized machine-learning techniques where all the local datasets are uploaded to one server, as well as to more classical decentralized approaches which often assume that local data samples are identically distributed. Federated learning enables multiple actors to build a common, robust machine learning model without sharing data, thus allowing to address critical issues such as data privacy, data security, data access rights and access to heterogeneous data.

[0103] The training of the ML model may be performed continuously or periodically, or in near real time as additional procedure data are made available. The training process involves algorithmically adjusting one or more ML model parameters (e.g., weights or bias at any particular layer of a neural network model), until the ML model being trained satisfies a specified training convergence criterion. By way of example and not limitation, the ML model may be trained with weighted square loss (for explicit feedback) or with binary cross-entropy loss (for implicit feedback). Other training techniques, such as deep factorization machine, wide and deep learning, deep structured semantic models, or autoencoder based recommender systems, may be used. Then, duringthe inference operation, the patient specific input features provided to the ML model may be propagated from an input layer, through one or more hidden layers, and ultimately to an output layer that corresponds to the laser output setting. During and / or subsequent to the inference operation, the laser output setting may be communicated to the user via the user interface (UI) and / or automatically cause the controller circuit 214 for performing a desired action.

[0104] FIG. 6 is a flowchart illustrating an example method 600 for identifying an anatomical target in a fluid surgical site using triggered collection of sensor data, and providing a laser treatment thereto. The anatomical target may include an anatomical structure (e.g., soft tissue, hard tissue, or abnormal such as cancerous tissue) or a calculus structure (e.g., kidney or pancreobiliary or gallbladder stone). The method 600 may be implemented in and executed by a laser treatment system, such as the laser treatment system 100 or a variant thereof, such as the surgical laser system 200 or the endoscopic laser lithotripsy system 300. Although the processes of the method 600 are drawn in one flowchart, they are not required to be performed in a particular order. In various examples, some of the processes can be performed in a different order than that illustrated herein.

[0105] At step 610, information about properties of an anatomical target may be sensed using one or more sensors. Examples of the sensors and the sensed information include a spectroscopic sensor to sense spectroscopic signal from the anatomical target, an imaging sensor to produce images or video frames of at least a portion of the anatomical target, or an acoustic sensor to sense an acoustic signal in response to an excitation signal (e.g., diagnostic laser pulses) delivered to the anatomical target, as described above with respect to FIGS. 2 and 3.

[0106] At step 620, triggered sensor data collection may be initiated in a presence of a trigger event indicative of laser pulses being delivered to the anatomical target. The trigger event may define an optimal time to initiate sensor data collection for use in in vivo target identification. In an example, the sensor data may be continuously recorded and buffered in a memory circuit, and the trigger event indicative of the laser pulses being delivered to the anatomical target may flag a reference timing for a portion of the continuously recorded andbuffered sensor data that may be used for in vivo target identification. The portion of the continuously recorded and buffered sensor data used for target identification may have a specific duration relative in time to the set flag or timestamp and prior to the laser pulses being incident on the anatomical target. In an example, the portion of the sensor data may begin at the set flag or timestamp. In another example, the portion of the sensor data may begin at a specific time prior to the set flag or timestamp. In yet another example, the portion of the sensor data may occur during a time period right before the set flag or timestamp. In some examples, the sensor data may be collected and stored a ring buffer with a fixed buffer size in accordance with a first-in first-out (FIFO) process. The ring buffer can help ensure efficient storage of the triggered sensor data with respect to the trigger event.

[0107] Examples of the trigger event that automatically triggers sensor data collection or flags the reference timing of sensor data portion for target identification may include a laser actuator activation event, such as detected food petal press. In another example, the trigger event includes a laser emission event, such as a detected emission of laser pulses at a specific wavelength or range of wavelength, such as in a range of 800 - 1100 nm in one example, or 1940 nm in the case of a thulium laser in another example. In yet another example, the trigger event includes a detected formation of vapor bubbles consequent to laser pulses incident on the target producing heat and vibration that promotes bubble formation. The formation of vapor bubbles may be detected using images or video frames of the target structure and surrounding anatomical environment sensed by an imaging sensor, or using acoustic feedback signal sensed by an acoustic sensor. In another example, the trigger event includes a motion or displacement of the anatomical target, which may be detected using an imaging sensor signal, or an acoustic signal.

[0108] At step 630, the anatomical target may be identified using the triggered collection of sensor data from step 620. The target identification includes identify a type or composition of the anatomical target. In an example, one or more spectroscopic, imaging, or acoustic properties may be used to identify the target anatomy as one of a plurality of structure categories, such as a category of calculi structure, or a category of anatomical structure. In anotherexample, the anatomical target may be identified as one of a plurality of structure types of the same category, such as a particular tissue type within an identified category of anatomical structure, or as a particular calculi type within an identified category of calculi structure.

[0109] At step 640, a laser output setting may be adjusted based at least in part on the identified type or composition of the anatomical target. In an example, the laser system may be set to operate in a first operating mode if the target is identified as a calculi structure, or a second operating mode if the target is identified as an anatomical structure, or a third operating mode if the target is identified as neither an anatomical structure nor a calculi structure. In an example, one or more irradiation parameters may be adjusted based on the identification of a calculi type, or based on the identification of a tissue type. In various examples, the laser output setting may be determined or adjusted using Al or ML based techniques. For example, information about the identified type or composition of the target structure may be applied to a trained ML model to automatically determine a proper laser setting to be used during the procedure. In some examples, sensor data collected from the one or more sensors in response to a trigger event, or a selected portion of continuously recorded and buffered sensor data with a specific duration that begins at a time relative to a trigger event (e.g., at, or right before, the trigger event) and ends prior to the laser pulses being incident on the anatomical target, may be applied directly to a trained ML model to output a proper laser setting. The ML model may be trained using a training set of sensor data that are collected or flagged in response to similar trigger events from multiple patients.

[0110] At step 650, laser pulses may be delivered to the anatomical target in accordance with the adjusted laser output setting.

[0111] FIG. 7 is a flowchart illustrating an example method 700 for identify an anatomical target using a selected portion of event-triggered sensor data. The method 700 is an embodiment of at least a portion, such as steps 610- 630, of the method 600.

[0112] At step 710, sensor data, such as those sensed by the one or more sensors 220 as described above with respect to FIG. 2, may be continuously collected during a medical procedure. At step 720, the collected sensor data maybe stored in a first buffer, such as a ring buffer that uses a first-in first-out (FIFO) data storing process. The ring buffer may have a fixed buffer size such that at any time, only the most recent sensor data get stored and retained in the buffer.

[0113] At step 730, a detection can be made to determine whether a trigger event, such as a foot pedal press, or any of the events 410, 420, 430, or 440 as described above with respect to FIG. 4, is present. If no trigger event is detected, then the sensor data collection can be continued at step 710, and the sensor data can be stored in the first buffer. If the trigger event is detected to be present, then at step 740 a flag or timestamp of the trigger event may be set in the stored data in the first buffer. Because the ring buffer has a fixed size and stores most recently collected data, more efficient storage of sensor data with respect to the detected trigger event can be achieved. As illustrated in FIG. 7, the detected presence of the trigger event may also activate storage of post-trigger sensor data (i.e., sensor data collected after the detected trigger event) in a second buffer at step 760. The second buffer may be similar to the first buffer in one example, or a different buffer in another example. Using the second buffer may help provide a continuous record of timeline of events throughout the procedure session.

[0114] The set flag or timestamp at step 740 may be used as a reference timing when identifying a portion of the stored sensor data from the first buffer for target identification. At step 750, a portion of the stored sensor data with a specific duration relative in time to the set flag or timestamp and prior to the laser pulses being incident on the anatomical target may be retrieved from the first buffer. The retrieved sensor data portion may be used for target identification, or for algorithm development. In an example, the retrieved sensor data may be used for constructing a training dataset used for training an Al or ML based prediction model. In an example, the retrieved sensor data portion may begin at the set flag or timestamp. In another example, the retrieved sensor data portion may begin at a specific time prior to the set flag or timestamp. In yet another example, the retrieved sensor data portion may occur during a time period right before the set flag or timestamp. In an example of collecting and storing spectroscopic scans using a ring buffer with a fixed size that can hold asmany as N spectroscopic scans, most recent M spectroscopic scans (M<=N) prior to the set flag or timestamp may be retrieved from the ring buffer and used for target identification or algorithm development.

[0115] FIG. 8 illustrates generally a block diagram of an example machine 800 upon which any one or more of the techniques (e.g., methodologies) discussed herein may perform. Portions of this description may apply to the computing framework of various portions of the laser treatment system 100 (e.g., the laser feedback control system 101), the surgical laser system 200, or the endoscopic laser lithotripsy system 300.

[0116] In alternative embodiments, the machine 800 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine 800 may operate in the capacity of a server machine, a client machine, or both in server-client network environments. In an example, the machine 800 may act as a peer machine in peer-to-peer (P2P) (or other distributed) network environment. The machine 800 may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein, such as cloud computing, software as a service (SaaS), other computer cluster configurations.

[0117] Examples, as described herein, may include, or may operate by, logic or a number of components, or mechanisms. Circuit sets are a collection of circuits implemented in tangible entities that include hardware (e.g., simple circuits, gates, logic, etc.). Circuit set membership may be flexible over time and underlying hardware variability. Circuit sets include members that may, alone or in combination, perform specified operations when operating. In an example, hardware of the circuit set may be immutably designed to carry out a specific operation (e.g., hardwired). In an example, the hardware of the circuit set may include variably connected physical components (e.g., execution units,transistors, simple circuits, etc.) including a computer readable medium physically modified (e.g., magnetically, electrically, moveable placement of invariant massed particles, etc.) to encode instructions of the specific operation. In connecting the physical components, the underlying electrical properties of a hardware constituent are changed, for example, from an insulator to a conductor or vice versa. The instructions enable embedded hardware (e.g., the execution units or a loading mechanism) to create members of the circuit set in hardware via the variable connections to carry out portions of the specific operation when in operation. Accordingly, the computer readable medium is communicatively coupled to the other components of the circuit set member when the device is operating. In an example, any of the physical components may be used in more than one member of more than one circuit set. For example, under operation, execution units may be used in a first circuit of a first circuit set at one point in time and reused by a second circuit in the first circuit set, or by a third circuit in a second circuit set at a different time.

[0118] Machine (e.g., computer system) 800 may include a hardware processor 802 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 804 and a static memory 806, some or all of which may communicate with each other via an interlink (e.g., bus) 808. The machine 800 may further include a display unit 810 (e.g., a raster display, vector display, holographic display, etc.), an alphanumeric input device 812 (e.g., a keyboard), and a user interface (UI) navigation device 814 (e.g., a mouse). In an example, the display unit 810, input device 812 and UI navigation device 814 may be a touch screen display. The machine 800 may additionally include a storage device (e.g., drive unit) 816, a signal generation device 818 (e.g., a speaker), a network interface device 820, and one or more sensors 821, such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensors. The machine 800 may include an output controller 828, such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).

[0119] The storage device 816 may include a machine readable medium 822 on which is stored one or more sets of data structures or instructions 824 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions 824 may also reside, completely or at least partially, within the main memory 804, within static memory 806, or within the hardware processor 802 during execution thereof by the machine 800. In an example, one or any combination of the hardware processor 802, the main memory 804, the static memory 806, or the storage device 816 may constitute machine readable media.

[0120] While the machine-readable medium 822 is illustrated as a single medium, the term "machine readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) configured to store the one or more instructions 824.

[0121] The term “machine readable medium” may include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine 800 and that cause the machine 800 to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Nonlimiting machine-readable medium examples may include solid-state memories, and optical and magnetic media. In an example, a massed machine-readable medium comprises a machine readable medium with a plurality of particles having invariant (e.g., rest) mass. Accordingly, massed machine-readable media are not transitory propagating signals. Specific examples of massed machine- readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EPSOM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0122] The instructions 824 may further be transmitted or received over a communication network 826 using a transmission medium via the network interface device 820 utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Examplecommunication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as WiFi®, IEEE 802.16 family of standards known as WiMax®), IEEE 802.15.4 family of standards, peer-to-peer (P2P) networks, among others. In an example, the network interface device 820 may include one or more physical jacks (e.g., Ethernet, coaxial, or phonejacks) or one or more antennas to connect to the communication network 826. In an example, the network interface device 820 may include a plurality of antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine 800, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.Additional Notes

[0123] The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the invention can be practiced. These embodiments are also referred to herein as “examples.” Such examples can include elements in addition to those shown or described. However, the present inventors also contemplate examples in which only those elements shown or described are provided. Moreover, the present inventors also contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.

[0124] In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term“or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In this document, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, composition, formulation, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.

[0125] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that such embodiments can be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

What is claimed is:

1. A surgical laser system, comprising: a laser system configured to emit laser pulses via a laser fiber to an anatomical target in a fluid surgical site of a patient; one or more sensors configured to sense information about properties of the anatomical target; and a controller circuit, comprising a feedback analyzer circuit configured to: initiate triggered collection of sensor data in a presence of a trigger event indicative of the laser pulses being delivered to the anatomical target; and identify a type or composition of the anatomical target using the triggered collection of sensor data, wherein the controller circuit is configured to determine or adjust a laser output setting of the laser system based at least in part on the identified type or composition of the anatomical target.

2. The surgical laser system of claim 1, comprising a memory circuit configured to continuously record and buffer sensor data sensed by the one or more sensors, wherein the feedback analyzer circuit is configured to, in the presence of the trigger event indicative of the laser pulses being delivered to the anatomical target: set a flag or timestamp of the trigger event on the continuously recorded and buffered sensor data; and identify the type or composition of the anatomical target using a portion of the continuously recorded and buffered sensor data with a specific duration relative in time to the set flag or timestamp and prior to the laser pulses being incident on the anatomical target.

3. The surgical laser system of claim 2, wherein the portion of the continuously recorded and buffered sensor data used for identifying the type or composition of the anatomical target begins at a specific time prior to the set flag or timestamp.

4. The surgical laser system of any of claims 2-3, wherein the portion of the continuously recorded and buffered sensor data used for identifying the type or composition of the anatomical target begins at the set flag or timestamp.

5. The surgical laser system of any of claims 2-4, wherein the memory circuit includes a ring buffer with a fixed buffer size, the ring buffer configured to continuously record and buffer the sensor data in accordance with a first-in first-out (FIFO) process, wherein the feedback analyzer circuit is configured to, in the presence of the trigger event, retrieve from the ring buffer a portion of the sensor data recorded therein prior to the set flag or timestamp.

6. The surgical laser system of any of claims 1-5, further comprising a light source configured to emit an electromagnetic radiation to the anatomical target, wherein the one or more sensors include a spectroscopic sensor configured to sense spectroscopic data from the anatomical target in response to the electromagnetic radiation at the anatomical target, wherein the feedback analyzer circuit is configured to initiate triggered collection of spectroscopic data in the presence of the trigger event.

7. The surgical laser system of any of claims 1-6, comprising a laser actuator operable by a user to manually activate the laser system to emit the laser pulses, wherein the trigger event includes a manual activation of the laser system to emit the laser pulses to the anatomical target.

8. The surgical laser system of claim 7, wherein the laser actuator includes a foot pedal configured to be pressed to activate the laser system to emit the laser pulses.

9. The surgical laser system of any of claims 1-8, comprising a laser detector configured to detect an emission of laser pulses,wherein the controller circuit is configured to initiate the triggered collection of sensor data in response to the detected emission of laser pulses.

10. The surgical laser system of claim 9, wherein the laser detector is configured to detect an emission of laser pulses at a specific wavelength, wherein the controller circuit is configured to initiate the triggered collection of sensor data in response to the detected emission of laser pulses at the specific wavelength.

11. The surgical laser system of claim 10, wherein the specific wavelength is in a range of 800 - 1 lOOnm.

12. The surgical laser system of any of claims 1-11, comprising a bubble detector configured to detect a formation of vapor bubbles in the fluid surgical site consequent to the laser pulses being incident on the anatomical target, wherein the controller circuit is configured to initiate the triggered collection of sensor data in response to the detected formation of vapor bubbles in the fluid surgical site.

13. The surgical laser system of claim 12, wherein the bubble detector is configured to detect the formation of vapor bubbles using images or video frames of the anatomical target and the fluid surgical site obtained by an imaging device of the surgical laser system.

14. The surgical laser system of claim 13, wherein the bubble detector is configured to detect the formation of vapor bubbles using an acoustic feedback signal traveling across at least a portion of the fluid surgical site and sensed by an acoustic sensor of the surgical laser system.

15. The surgical laser system of any of claims 11-14, comprising a motion detector configured to detect a motion or displacement of the anatomical target consequent to an emission of laser pulses to the anatomical target,wherein the controller circuit is configured to initiate the triggered collection of sensor data in response to the detected motion or displacement of the anatomical target.

16. The surgical laser system of claim 15, wherein the motion detector is configured to detect the motion or displacement of the anatomical target using images or video frames of the anatomical target obtained by an imaging device of the surgical laser system.

17. The surgical laser system of any of claims 1-16, wherein to identify the type or composition of the anatomical target includes to identify a calculi target with one or more compositions, wherein the controller circuit is configured to determine or adjust the laser output setting of the laser system based at least in part on the identified calculi target, and to control the laser system to emit the laser pulses to the calculi target under the adjusted laser output setting to ablate or fragment the calculi target.

18. The surgical laser system of any of claims 1-16, wherein to identify the type or composition of the anatomical target includes to identify the anatomical target as a treatment target or a non-treatment target, wherein the controller circuit is configured to control the laser system to enable emission of laser pulses to the treatment target, and to disable emission of laser pulses to the non-treatment target.

19. A method of identifying an anatomical target in a fluid surgical site of a patient and providing laser treatment thereof via a laser system, the method comprising: sensing information about properties of the anatomical target using one or more sensors; initiating triggered collection of sensor data in a presence of a trigger event indicative of laser pulses being delivered to the anatomical target;identifying a type or composition of the anatomical target using the triggered collection of sensor data; adjusting a laser output setting of the laser system based at least in part on the identified type or composition of the anatomical target; and providing laser pulses to the anatomical target via a laser system in accordance with the adjusted laser output setting.

20. The method of claim 19, comprising: continuously recording and buffering sensor data sensed by the one or more sensors in a memory circuit; and in the presence of the trigger event indicative of the laser pulses being delivered to the anatomical target, setting a flag or timestamp of the trigger event on the continuously recorded and buffered sensor data; wherein identifying the type or composition of the anatomical target includes using a portion of the continuously recorded and buffered sensor data with a specific duration relative in time to the set flag or timestamp and prior to the laser pulses being incident on the anatomical target.

21. The method of claim 20, wherein the portion of the continuously recorded and buffered sensor data used for identifying the type or composition of the anatomical target begins at a specific time prior to the set flag or timestamp.

22. The method of any of claims 20-21, wherein the portion of the continuously recorded and buffered sensor data used for identifying the type or composition of the anatomical target begins at the set flag or timestamp.

23. The method of any of claims 20-22, wherein continuously recording and buffering the sensor data is performed in accordance with a first-in first-out (FIFO) process in a ring buffer with a fixed buffer size, wherein the portion of the continuously recorded and buffered sensor data used for identifying the type or composition of the anatomical target occurs prior to the set flag or timestamp.

24. The method of any of claims 19-23, wherein sensing the information about properties of the anatomical target includes sensing spectroscopic data from the anatomical target via a spectroscopic sensor in response to electromagnetic radiation at the anatomical target, wherein the triggered collection of sensor data includes triggered collection of spectroscopic data in the presence of the trigger event.

25. The method of any of claims 19-24, wherein the trigger event includes a manual activation of the laser system via a laser actuator to emit the laser pulses to the anatomical target.

26. The method of any of claims 19-25, comprising detecting emission of laser pulses at a specific wavelength, wherein the trigger event includes the detected emission of the laser pulses at the specific wavelength.

27. The method of any of claims 19-26, comprising detecting a formation of vapor bubbles in the fluid surgical site consequent to the laser pulses incident on the anatomical target based on images or video frames of, or acoustic feedback signal from, the anatomical target, wherein the trigger event includes the detected formation of vapor bubbles in the fluid surgical site.

28. The method of any of claims 19-27, comprising detecting a motion or displacement of the anatomical target consequent to emission of laser pulses to the anatomical target, wherein the trigger event includes the detected motion or displacement of the anatomical target.

29. The method of any of claims 19-28, wherein identifying the type or composition of the anatomical target includes identifying a calculi target with one or more compositions,wherein adjusting a laser output setting is based at least in part on the identified calculi target.

30. The method of any of claims 19-28, wherein identifying the type or composition of the anatomical target includes identifying the anatomical target as a treatment target or a non-treatment target, wherein adjusting a laser output setting includes enabling emission of laser pulses to the treatment target and disabling emission of the laser pulses to the non-treatment target.

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