Tissue sampling needle with sensor

Abstract

A biological substance sampling system is described, where the system includes a needle defining a lumen and a having a longitudinal axis, wherein the lumen is configured to receive a biological substance, a sensor positioned along the longitudinal axis of the needle, the sensor configured for generating sensor data, and a processor configured for receiving the sensor data and detecting, based on the sensor data, a presence of the biological substance in the lumen.

Description

TISSUE SAMPLING NEEDLE WITH SENSORPRIORITY CLAIM

[0001] This application claims the benefit of priority to U.S. Provisional Application Serial No. 63 / 609,435, filed December 13, 2023, the contents of which are hereby incorporated by reference.FIELD OF THE DISCLOSURE

[0002] This document pertains generally, but not by way of limitation, to biopsy needles.BACKGROUND

[0003] Needle biopsies are a common medical procedure used to extract biological substance samples for examination and diagnostic testing. In a typical biopsy, a hollow needle is inserted into the body to collect a small sample from an organ or other site of interest. The extracted sample is then analyzed to check for disease or other abnormalities.

[0004] Biopsy needles enable physicians to access biological substance at remote or hard- to-reach locations within the body. Various needle designs exist to target different organs and biological substance types. The needle diameter, tip shape, and other features may be tailored for specific biopsy procedures.

[0005] After inserting the biopsy needle, biological substance samples are collected within the hollow bore of the needle. Physicians use technique and experience to obtain adequate biological substance for testing. Multiple samples may be extracted in a single biopsy session to ensure sufficient material is collected for analysis.

[0006] The biological substance samples extracted by biopsy needles provide valuable diagnostic information for many medical conditions. Analysis of biopsy samples may detect cancer, infection, and other pathologies. Biopsy -based diagnosis provides important guidance for treatment decisions and disease management.SUMMARY OF THE DISCLOSURE

[0007] This disclosure describes, among other things, techniques to determine the presence of biological substance in a lumen of a biopsy needle.

[0008] In some aspects, this disclosure is directed to a biological substance sampling system, comprising: a needle defining a lumen and a having a longitudinal axis, wherein the lumen is configured to receive a biological substance; a sensor positioned along thelongitudinal axis of the needle, the sensor configured for generating sensor data; and a processor configured for receiving the sensor data and detecting, based on the sensor data, a presence of the biological substance in the lumen.

[0009] In some aspects, this disclosure is directed to a method for sampling biological substances using a needle defining a lumen and a having a longitudinal axis, wherein the lumen is configured to receive biological substance, the method comprising: receiving sensor data from a sensor positioned along the longitudinal axis of the needle; and detecting, based on the sensor data, a presence of the biological substance in the lumen.

[0010] In some aspects, this disclosure is directed to a biological substance sampling system, comprising: a needle defining a lumen and a having a longitudinal axis, wherein the lumen is configured to receive a biological substance; a fiber Bragg grating sensor positioned along the longitudinal axis of the needle, the fiber Bragg grating sensor configured for generating pressure sensor data; and a processor configured for receiving the pressure sensor data and detecting, based on the pressure sensor data, a presence of the biological substance in the lumen.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document.

[0012] FIG. 1 depicts an example of a biopsy system including an ultrasonic biopsy needle, which may implement various techniques of this disclosure.

[0013] FIG. 2 is a sectional view of a distal end portion of the ultrasonic biopsy needle of FIG. 1.

[0014] FIG. 3 is an example of a light sensing system including a fiber Bragg grating sensor.

[0015] FIG. 4 is simplified version of the ultrasonic biopsy needle of FIG. 2 depicting the sensor positioned to sense an environmental characteristic within the lumen of the needle.

[0016] FIG. 5 is another simplified version of the ultrasonic biopsy needle of FIG. 2 depicting the sensor positioned to sense an environmental characteristic at an exterior surface of the needle.

[0017] FIG. 6 is another simplified version of the ultrasonic biopsy needle of FIG. 2 depicting two sensors to sense corresponding environmental characteristics.

[0018] FIG. 7 is a schematic diagram of an example of a computer-based tissue sample analyzer.

[0019] FIG. 8 shows a schematic diagram of an example of a trained machine learning model.

[0020] FIG. 9 depicts an example of a portion of the biopsy needle 1 of FIG. 1 and FIG. 2.

[0021] FIG. 10 is a flowchart of an example of a method for sampling biological substance using a biopsy needle.

[0022] FIG. 11 is a block diagram illustrating an example of a machine upon which one or more examples may be implemented.DETAILED DESCRIPTION

[0023] The present inventor has recognized that one challenge with current biopsy needles is the lack of feedback during the biological substance sampling process. Physicians rely on technique and experience to determine if an adequate sample has been collected, especially when sampling biological substance at remote or hard-to-access sites in the body. With no confirmation that biological substance has been captured, the biopsy procedure may need to be repeated, causing additional discomfort and risk for the patient.

[0024] Some existing biopsy systems utilize sensors to measure the force on the needle tip and / or utilize optical coherence tomography to image a biopsy needle entering the sample. However, these devices still have limited ability to indicate successful biological substance capture within the needle lumen or determine the amount of biological substance collected. The present inventor has recognized that a need remains for improved sensor integration into biopsy needles that may provide physicians with real-time feedback on acquiring biological substance samples.

[0025] This disclosure describes, among other things, techniques to determine the presence of biological substance in a lumen of a biopsy needle.

[0026] FIG. 1 depicts an example of a biopsy system including an ultrasonic biopsy needle, which may implement various techniques of this disclosure. The biopsy system 150 includes an ultrasonic biopsy needle 1 and an ultrasonic endoscope 100. The ultrasonic biopsy needle 1 (also simply referred to as a “needle 1”) is a puncture needle used forbiopsy in combination with an ultrasonic endoscope 100. The biopsy system 150 is an example of a biological substance sampling system.

[0027] The ultrasonic endoscope 100 may be a thin-diameter endoscope, such as used to diagnose or treat a respiratory organ. The ultrasonic endoscope 100 includes an inserting portion 101, an operation portion 109, a universal cord 112, an optical source device 113, an optical observation unit 114, and an ultrasonic observation unit 115. The inserting portion 101 is inserted into a human body from a distal end of the inserting portion 101. The operation portion 109 is mounted to a proximal end of the inserting portion 101. One end of the universal cord 112 is connected to a side portion of the operation portion 109. The optical source device 113 is connected to the other end of the universal cord 112 via a branch cable 112a. The optical observation unit 114 is connected to the other end of the universal cord 112 via a branch cable 112Z>. The ultrasonic observation unit 115 is connected to the other end of the universal cord 112 via a branch cable 112c.

[0028] A distal end rigid portion 102, a bending portion 105, and a flexible tubular portion 106 are provided side by side in the inserting portion 101 sequentially from a distal end side of the inserting portion 101. The distal end rigid portion 102 includes an optical imagecapturing mechanism 103 for optical observation, and an ultrasonic scanning mechanism 104 for ultrasonic observation.

[0029] The optical image-capturing mechanism 103 includes various configuration elements such as an image-capturing optical system, an image sensor (for example, a CCD or a CMOS), and a CPU. The visual field of the image-capturing optical system is diagonally oriented to a front side of the distal end rigid portion 102. The image sensor detects an image of an object which is incident thereto via the image-capturing optical system. The CPU controls the operation of the image sensor.

[0030] The ultrasonic scanning mechanism (probe) 104 includes an ultrasonic transducer (not shown) emitting and receiving an ultrasonic wave. An ultrasonic wave emitted by the ultrasonic transducer collides with and is reflected from an observation target, and the ultrasonic transducer receives the reflected wave. The ultrasonic scanning mechanism 104 outputs a signal to the ultrasonic observation unit 115 based on the ultrasonic wave received by the ultrasonic transducer. The ultrasonic scanning mechanism 104 in the embodiment is used to acquire an ultrasonic image of a tissue that is a biopsy target, and an ultrasonic image of a needle tube 3 (shown in FIGS. 2, 4, 5, and 6) during a biopsy procedure.

[0031] The bending portion 105 is formed into a cylindrical shape, and may be bent in a predetermined direction by pulling a steering cable (not shown) of the bending portion 105,and extending to the operation portion 109 via the operation portion 109. The bending portion 105 may be bent in two directions along a scanning direction of an ultrasonic wave.

[0032] In some examples, an endoscope which includes an inserting portion having a thin outer diameter and may be bent in two directions is used to treat a respiratory organ. However, an endoscope which has a thick outer diameter but offers a high degree of freedom in operation and may be bent in four directions may be used to treat a digestive organ.

[0033] The flexible tubular portion 106 is a cylindrical flexible member capable of guiding the distal end rigid portion 102 to a desired position in a luminal tissue or a body cavity.

[0034] A channel 107, and a tubular path (not shown) through which air or water is blown and suctioned, are provided inside the bending portion 105 and the flexible tubular portion 106.

[0035] The channel 107 shown in FIG. 1 is a cylindrical portion into which the biopsy needle 1 is inserted. One end of the channel 107 opens in the vicinity of a distal end portion of the distal end rigid portion 102, and the other end of the channel 107 opens in a side surface of the operation portion 109 on a distal end side. A flange-shaped proximal end connector 108 is fixed to the other end of the channel 107. The biopsy needle 1 used together with the ultrasonic endoscope 100 may be fixed to the proximal end connector 108. The channel 107 in the embodiment has an inner diameter of equal to or greater than 2.0 mm and equal to and less than 2.2 mm. The channel 107 may have an inner diameter smaller than the inner diameter of a channel of an endoscope for a digestive organ.

[0036] The operation portion 109 shown in FIG. 1 includes an outer surface that is formed such that an operator, a user of the ultrasonic endoscope 100, may hold the operation portion 109 with a hand. The operation portion 109 includes a bending operation mechanism 110 and multiple switches 111. The bending operation mechanism 110 is capable of bending the bending portion 105 by pulling the steering cable. Air or water is blown or suctioned through the tubular path by operating the multiple switches 111.

[0037] The optical source device 113 is a device emitting illumination light required for the optical image-capturing mechanism 103 to capture an image of a target. The optical observation unit 114 is configured to project an image that is captured by the image sensor of the optical image-capturing mechanism 103 on a monitor 116. The ultrasonic observation unit 115 receives a signal output from the ultrasonic scanning mechanism 104, generates an image based on the received signal, and projects the generated image on the monitor 116.

[0038] FIG. 2 is a sectional view of a distal end portion of the ultrasonic biopsy needle of FIG. 1. The biopsy needle 1 includes an inserting body 2 inserted into a human body, the operation portion (treatment tool operating portion) 8 (shown in FIG. 1) for operating the inserting body 2, and a stylet (core bar) 27. The biopsy needle 1 extends along a longitudinal axis 203.

[0039] The inserting body 2 is a long member which may be mounted into the channel 107 so as to be able to protrude from the distal end of the inserting portion 101 of the ultrasonic endoscope 100. The inserting body 2 includes the needle tube 3, and a cylindrical sheath 7 into which the needle tube 3 is inserted. The needle tube 3 is a cylindrical member, such as with a 22-gauge size, that is advanced and retracted by the operation portion 8.

[0040] The needle tube 3 may be made of a material that has flexibility and elasticity by which the needle tube 3 is easily restored to its straight state even if the needle tube 3 is bent by an external force. For example, alloy materials such as a stainless alloy, a nickeltitanium alloy, and a cobalt-chromium alloy may be adopted as the material of the needle tube 3.

[0041] An opening 31 is included at a distal end of the needle tube 3. The opening 31 is sharpened so as to enable the needle tube 3 to puncture a tissue, and the tissue is suctioned into the needle tube 3 through the opening 31. The opening 31 provided at the distal end of the needle tube 3 is formed by cutting a distal end of a tubular member (which forms the needle tube) off diagonally to an axial line of the tubular member. The opening 31 is sharply formed to be able to incise a biological tissue. The specific shape of the opening 31 may be appropriately selected from various well-known shapes while a tissue which is a target is taken into consideration.

[0042] As shown in FIG. 2, the sheath 7 includes a distal end coil 71 forming a distal end portion of the sheath 7, a proximal end coil 72 forming a proximal end portion of the sheath 7, a connecting portion 73, and a resin coating 74. The connecting portion 73 is a cylindrical member through which the distal end coil 71 is connected to the proximal end coil 72. The connecting portion 73 is fixed to a proximal end portion of the distal end coil 71 and to a distal end portion of the proximal end coil 72 by brazing.

[0043] For ultrasonic observation, it may be desirable to include an ultrasound sensor on the catheter that is in the scope working channel. As an example, the sheath 7 includes an ultrasonic scanning mechanism similar to the ultrasonic scanning mechanism 104 of FIG. 1. This may be in addition to the endoscope ultrasonic scanning mechanism 104 or a substitution for endoscope ultrasonic scanning mechanism 104.

[0044] The stylet 27 is positioned within a lumen 202 defined by the needle tube 3. The stylet 27 prevents a biological substance, e.g., tissue, fluid, or both, from fully entering the lumen 202. A user, e.g., a clinician, may control when the biological substance enters the lumen 202 by adjusting, e.g., at least partially retracting, the stylet proximally.

[0045] Additional information regarding the biopsy system 150 of FIG. 1 may be found in published US patent application publication number US20160206297, titled “Ultrasonic Biopsy Needle” to Uemichi et al., the entire contents of which being incorporated herein by reference.

[0046] As mentioned above, this disclosure describes, among other things, techniques to determine the presence of biological substance in a lumen of a biopsy needle, such as the lumen 202 of the biopsy needle 1. The biological substance sampling system includes a sensor 204 positioned along the longitudinal axis 203 of the needle, and a processor configured for receiving sensor data from the sensor and detecting, based on the sensor data, a presence of the biological substance in the lumen. In some examples, the system may detect a characteristic of the biological substance and use that characteristic to determine a type of the biological substance.

[0047] In some examples, the sensor 204 is configured to be positioned to sense an environmental characteristic within the lumen of the needle 1. For example, the sensor 204 may include a pressure sensor, a temperature sensor, or be configured to sense other environmental characteristics, such as capacitance, pH level, and conductivity. In other examples, the sensor 204 is positioned to sense the environmental characteristics at an exterior surface of the needle.

[0048] In some examples, the sensor 204 includes a fiber Bragg grating (FBG) sensor, such as shown in FIG. 3. An FBG sensor is an optical sensing technology gaining use in medical devices and diagnostics. FBGs contain a periodic variation of refractive index within the fiber core, acting as a wavelength-specific dielectric mirror. FBG sensors measure strain and temperature by detecting shifts in the reflected wavelengths.

[0049] FBGs allow for the creation of multiple, discrete sensors along the length of a single optical fiber. In addition, FBG sensors are biocompatible, immune to electromagnetic interference, and suitable for sterilization. These properties make FBGs well-suited for incorporation into biopsy needles and other invasive medical tools.

[0050] FIG. 3 is an example of a light sensing system including a fiber Bragg grating sensor that may be used with the biopsy system 150 of FIG. 1. The light sensing system 300 includes a light source 302 (which may be the same or similar to the optical sourcedevice 113 in FIG. 1), a coupler 304, and a reflector 306. As shown in FIG. 3, light emitted from a light source 302 is split by the coupler 304 and supplied to a reflector 306 and an optical fiber sensor 308 (that is positioned along the longitudinal axis 203 of the biopsy needle 1, as shown in FIG. 2). The optical fiber sensor 308 is an example of the sensor 204 of FIG. 2.

[0051] The optical fiber sensor 308 has a positive integer “n” of FBG sensor sections 310- 314 (or fiber Bragg gratings) and each sensor section may generate corresponding sensor data. In FIG. 3, the difference between the distance from the light source 302 to the FBG sensor sections 310-314 and the distance from the light source 302 to the reflector 306 is LI, L2, . . . , Ln, and LN is the difference between the distance from the light source 302 to the termination of the optical fiber sensor 308 and the distance from the light source 302 to the reflector 306. In the optical fiber sensor 308, the interval difference between the distance of each of the n FBG sensor sections to the coupler 304 and the distance from the coupler 304 to the reflector 306 differs from each other. Optical fiber sensors are well known and the theory of operation will not be described in detail. Further information regarding the FBG sensors may be found in published US patent application publication number US20110218404, titled “Light sensing system and endoscope system” to Katsumi Hirakawa, the entire contents of which being incorporated herein by reference.

[0052] The light sensing system 300 includes a processor 318 coupled with a memory 320. The memory 320 stores instructions that are executable by the processor 318. In some examples, the memory 320 may form part of the processor 318. The processor 318 is coupled with the coupler 304 and configured to ultimately receive data representing the sensor data from the optical fiber sensor 308 and any other sensors 204 included with the biopsy system, such as the biopsy system 150 of FIG. 1, and configured to generate sensor data.

[0053] The n fiber Bragg gratings may act as mirrors that may be partially reflective such as for a specific range of wavelengths of light passing through a fiber core, where the light reflected from the FBGs is referred to in this disclosure generally as an example of “sensor data”. Generally, the reflectivity of each grating of a particular pair of gratings will be substantially similar to the other grating in that particular pair of gratings, but may differ between gratings of a particular pair of gratings for particular implementations, or between different pairs of gratings, or both. This interferometric arrangement of FBGs may be capable of discerning the “optical distance or optical pathlength” between FBGs with extreme sensitivity. The “optical distance or pathlength” may be a function of the effective refractive index of the material of fiber core as well as the physical distance between FBGs.Thus, a change in the refractive index may induce a change in optical path length, even though the physical distance between FBGs has not substantially changed.

[0054] An interferometer, such as may be provided by the optical fiber sensor 308, may be understood as a device that may measure the interference between light reflected from each of the partially reflective FBGs 310-314, e.g., the sensor data. When the optical path length between the FBGs 310-314 is an exact integer multiple of the wavelength of the optical signal in the optical fiber core, then the light that passes through the optical fiber sensor 308 will be a maximum and the light reflected will be a minimum, such that the optical signal may be substantially fully transmitted through the optical fiber sensor 308. This addition or subtraction of grating-reflected light, with light being transmitted through the optical fiber core, may be conceptualized as interference.

[0055] The occurrence of full transmission or minimum reflection may be called a “null” and may occur at a precise wavelength of light for a given optical path length. Measuring the wavelength at which this null occurs may yield an indication of the length of the optical path between the two partially reflective FBGs 310-314. In such a manner, an interferometer, such as may be provided by the optical fiber sensor 308, may sense a small change in distance, such as a change in the optical distance between FBGs resulting from a received change in an environmental characteristic, such as pressure, temperature, etc. In this manner, one or more optical fiber sensors 308 may be positioned to sense an environmental characteristic, such as changes in pressure, temperature, or other environmental characteristics, within a lumen of the needle 1. In other examples, one or more optical fiber sensors 308 may be positioned to sense an environmental characteristic, such as changes in pressure, temperature, or other environmental characteristics, at an exterior surface of the needle 1.

[0056] The processor 318 is configured for receiving the sensor data from a sensor, such as the sensor 204, and detecting, based on the sensor data, a presence of the biological substance in the lumen. For example, the sensor 204 may include an optical fiber sensor 308 configured to operate as a pressure sensor. The pressure sensor generates first sensor data that corresponds to changes in pressure along the longitudinal axis of the needle 1, such as when a biological substance, e.g., blood, tissue, and the like, enters the opening 201 of the needle 1 during an advancement of the needle 1 into the biological substance. In this manner, the processor 318 detects, based on first sensor data, the presence of the biological substance in the lumen of the needle during an advancement of the needle.

[0057] In another example, it may be desirable for the clinician to know the size of the sample in the lumen of the needle. For example, the processor 318 may estimate the size of the biological substance within the lumen based on how many of the FBG sensor sections 310-314 (or fiber Bragg gratings) of the optical fiber sensor 308 are providing abovebaseline readings. For example, if there are ten FBG sensor sections that are spaced 0.5 mm apart and the four distal-most FBG sensor sections are indicating above-baseline readings, then the distance of the fourth to distal-most FBG sensor section to the distal tip would be indicative of the sample size.

[0058] In another example, a differential between and empty needle and a needle with a biological substance may be used. For example, as blood or intracellular fluid within the needle moves quickly along the length of the sensor, at a rate faster than the needle is being advanced, the processor 318 may detect two signals: one low pressure and one higher pressure, where the low-pressure reading is the fluid moving and the higher pressure relates to the tissue. The processor 318 may detect the two signals, filter out the noise of the fluid, and output to the user the presence of the higher pressure signal. The processor 318 may distinguish between the fluid and solid tissues, for example, either by the pressure, the speed that it moved, or both as a comparator to other solids. Tissues like fat are softer and have a different pressure signature than tissues like tumor or muscle tissue, which may be dense. The processor 318 may also be able to distinguish between muscle tissue and tumor tissue based on the pressure signatures. In some examples, both temperature and pressure may be used to help differentiate between different solid tissue types.

[0059] It may be desirable for the clinician to know that the biological substance has remained in the lumen of the needle during a retraction of the needle. During a retraction of the needle, the optical fiber sensor 308 that is configured to operate as a pressure sensor generates second sensor data that corresponds to changes in pressure along the longitudinal axis of the needle 1, such as when a biological substance, e.g., blood, tissue, and the like, exits the opening 201 of the needle 1 during a retraction of the needle 1. The processor 318 may monitor the second sensor data and determine whether any of the previously acquired biological substance has exited the opening 201. For example, a change between the first sensor data and the second sensor data may indicate that at least some of the biological substance has exited the opening 201 during the retraction of the needle. In this manner, the processor 318 detects, based on second sensor data, the presence of the biological substance in the lumen of the needle during the retraction of the needle.

[0060] FIG. 4 is simplified version of the ultrasonic biopsy needle of FIG. 2 depicting the sensor positioned to sense an environmental characteristic within the lumen of the needle.In FIG. 5, the stylet 27 has been adjusted, e.g., retracted proximally, and is therefore not depicted. The sensor 204, such as the optical fiber sensor 308 of FIG. 3, is shown positioned along the longitudinal axis of the needle to sense an environmental characteristic within the lumen 202. For example, the sensor 204 may be positioned with the lumen 202, such as to sense a pressure, temperature, or other environmental characteristic.

[0061] FIG. 5 is another simplified version of the ultrasonic biopsy needle of FIG. 2 depicting the sensor positioned to sense an environmental characteristic at an exterior surface of the needle. In FIG. 5, the stylet 27 has been adjusted, e.g., retracted, and is therefore not depicted. The sensor 204, such as the optical fiber sensor 308 of FIG. 3, is shown positioned along the longitudinal axis of the needle to sense an environmental characteristic at an exterior surface 502 of the needle. For example, the sensor 204 may be coupled with an exterior surface 502 of the needle, such as to sense a pressure, temperature, or other environmental characteristic.

[0062] FIG. 6 is another simplified version of the ultrasonic biopsy needle of FIG. 2 depicting two sensors to sense corresponding environmental characteristics. In FIG. 6, the stylet 27 has been adjusted, e.g., retracted, and is therefore not depicted. A first sensor 602, such as the optical fiber sensor 308 of FIG. 3, is shown positioned along the longitudinal axis of the needle to sense an environmental characteristic at an exterior surface 502 of the needle. For example, the first sensor 602 may be coupled with an exterior surface 502 of the needle, such as to sense a pressure, temperature, or other environmental characteristic. In addition, a second sensor 604 , such as the optical fiber sensor 308 of FIG. 3, is shown positioned along the longitudinal axis of the needle to sense an environmental characteristic within the lumen 202.

[0063] In some examples, a plurality of sensors are longitudinally offset from one another along the longitudinal axis. For example, FIG. 6 depicts the first sensor 602 and a third sensor 606 longitudinally offset from one another along the longitudinal axis 203. Although only two sensors are shown longitudinally offset from one another in FIG. 6, other configurations may include more than two sensors.

[0064] In addition, although the plurality of longitudinally offset sensors are shown positioned to sense an environmental characteristic at an exterior surface of the needle in FIG. 6, in other examples, the plurality of longitudinally offset sensors may be positioned to sense an environmental characteristic within the lumen of the needle. In yet other examples, there may be a plurality of sensors positioned to sense an environmental characteristic within the lumen of the needle and positioned to sense an environmentalcharacteristic at an exterior surface of the needle. In some examples, at least a first one of the sensors in FIG. 6 includes a pressure sensor and at least a second of the sensors in FIG.6 includes a temperature sensor.

[0065] It may be desirable for the clinician to know a type of the biological substance, such as whether the biological substance includes muscle, tissue, fat, etc., which may be used to distinguish biological substances from one another and provide the clinician with increased confidence that they have extracted and removed the intended biological substance. Using techniques of this disclosure, a processor determines, based on sensor data received from a sensor, a characteristic of the biological substance. Then, the processor determines, based on the determined characteristic, a type of the biological substance, e.g., muscle, tissue, fat, etc. These techniques are described below with respect to FIG. 7. For example, the processor 318 of FIG. 3 determines, based on sensor data received from the sensor 204 of FIG. 4, a characteristic of the biological substance, such as density, conductivity, and / or other characteristic.

[0066] FIG. 7 shows a schematic diagram of an example of a computer-based tissue sample analyzer. The computer-based biological substance sample analyzer 700 is configured for, among other things, determining a characteristic of a biological substance in the lumen of the needle 1 based on the sensor data, and determining, based on the determined characteristic, a type of the biological substance, based on input from one or more of the components of the biopsy system 150 of FIG. 1, including the light sensing system 300 of FIG. 3.

[0067] In some examples, the computer-based biological substance sample analyzer 700 may include an input interface 702 through which medical information, such as age, weight, sex, that are specific to a patient may be provided as input features to a trained machine learning (ML) or artificial intelligence (Al) model, such as trained Al model 704. One or more relevant input features 710, which may be extracted from various component outputs 712, are applied to the Al model to generate an output predicted from Al model inference 706.

[0068] For example, one or more relevant input features 710, which may be extracted from the sensor data generated by the sensors of the light sensing system 300 of FIG. 3, may be applied to the trained Al model 704, and the Al model 704 may determine a characteristic of a biological substance in the lumen of the needle 1, e.g., a density of the biological substance and determine. Then, based on the determined characteristic, the Al model 704 determines a type of the biological substance. For example, the Al model 704 maydetermine that the type of the biological substance is muscle, fat, or some other biological substance based on the determined characteristic. The computer-based biological substance sample analyzer 700 may provide one or more confidence scores 714. The confidence scores 714 and the output predicted from Al model inference 706, e.g., type of biological substance, may be displayed on a user interface 716 and communicated to a user, e.g., a clinician.

[0069] In some embodiments, the input interface 702 may be a direct data link between the computer-based biological substance sample analyzer 700 and one or more medical devices (e.g., light sensing system 300), that generates at least some of the input features. Additionally, or alternatively, the input interface 702 may be a classical user interface that facilitates interaction between a user and the computer-based biological substance sample analyzer 700. For example, the input interface 702 may facilitate a user interface through which the user may manually enter information.

[0070] Based on one or more of the input features, the output predicted from Al model inference 706 performs an inference operation using the Al model 704 to generate a characteristic of a biological substance in the lumen of the needle 1, e.g., a density of the biological substance and determine, based on the determined characteristic, a type of the biological substance. For example, input interface 702 may deliver sensor data into an input layer of the Al model 704, which propagates these input features through the Al model 704 to an output layer. The Al model 704 may 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 Al model 704 explores the study and construction of algorithms (e.g., machine-learning algorithms) that may learn from existing data and make predictions about new data. Such algorithms operate by building an Al model from example training data in order to make data-driven predictions or decisions expressed as outputs or assessments.

[0071] There are two common modes for machine learning (ML): supervised ML and unsupervised ML. Supervised ML 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 ML 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 may implement the same relationships when given inputs to generate the corresponding outputs. Unsupervised ML is the training of an ML algorithm using information that is neither classified nor labeled, and allowing the algorithm to act on that information withoutguidance. Unsupervised ML is useful in exploratory analysis because it may automatically identify structure in data.

[0072] Common tasks for supervised ML are classification problems and regression problems. Classification problems, also referred to as categorization problems, aim at classifying items into one of several category values (for example, is this object an apple or an orange?). 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).

[0073] Some common tasks for unsupervised ML include clustering, representation learning, and density estimation. Some examples of commonly used unsupervised-ML algorithms are K-means clustering, principal component analysis, and auto-encoders.

[0074] 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 machinelearning 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.

[0075] In some examples, the Al model may be trained continuously or periodically prior to performance of the inference operation by the output predicted from Al model inference 706. Then, during the inference operation, the patient-specific input features provided to the Al model may be propagated from an input layer, through one or more hidden layers, and ultimately to an output layer.

[0076] By using these techniques, a processor, such as the processor 318 of FIG. 3, may determine, based on the sensor data from a sensor, such as the optical fiber sensor 308 of FIG. 3, a characteristic of a biological substance, either within the lumen or adjacent an exterior surface of the needle 1, depending on the positioning of the sensor(s). The processor may then determine, based on the determined characteristic, a type of the biological substance.

[0077] In addition, in some examples, the processor is configured for detecting, based on the sensor data, a change in a type of biological substance. For example, as the biopsyneedle 1 is advanced through the body of a patient, the biopsy needle 1 may encounter different types of biological substances. The clinician may want to biopsy a particular type of biological substance. As described above, the processor, such as the processor 318 of FIG. 3, may determine the type of biological substance, and further detect when there is a change in type. The processor is further configured for displaying, on the user interface, such as the monitor 116 of FIG. 1, an indication to a user to adjust a stylet positioned within the lumen of the needle so as to permit the biological substance to enter the lumen. For example, once the processor determines that the needle 1 has encountered the particular type of biological substance, the processor may provide an indication on a user interface to indicate that a position of the stylet, such as the stylet 27 of FIG. 2, should be adjusted within the lumen of the needle, e.g., at least partially retracted, so as to permit the biological substance to enter the lumen.

[0078] FIG. 8 shows a schematic diagram of an example of a trained machine learning model 800. Various sensors may be used to generate sensor data. For example, an impedance sensor 802 is used to generate electrical impedance data 804, a pressure sensor 806, e.g., an optical fiber sensor 308 of FIG. 3, is used to generate pressure data 808, and / or an ultrasound imaging device 810, e.g., the ultrasonic endoscope 100 of FIG. 1, is used to generate imaging data 812.

[0079] The sensor data from the one or more sensors is used to generate N sets of signal training data 814, such as one or more of pressure data N 816, electrical impedance data N 818, and / or imaging data N 820.

[0080] One or more signal processing steps may be performed on the signal training data 814, such as sampling, feature extraction, filtering, and the like, before the signal data is ready to be used as training data 822. The training data 822 may include N sets of training data based on the signal training data 814. In addition, the training data 822 may include patient data N. For example, annotation training data 824 may include sets of patient data N 826 and labeling data N 828. The patient data N 826 may include patient information such as age, sex, weight, and / or known medical conditions of a patient 830. The labeling data N 828 may be generated by a medical practitioner 832. The labeling data N 828 may include location labels and tissue type labels, for example.

[0081] The training data 822 is used to train an Al or machine learning model, such as the trained machine learning model 800, e.g., the Al model 704 of FIG. 7. The training data 822 may be applied to a neural network structure 834, such as a DNN, comprising an input layer, one or more hidden layers, and an output layer. The training data 822, along withlabeling data N 828, may be fed into the input layer of the neural network structure 834, which propagates the input data or data features through one or more hidden layers to the output layer that outputs weights and bias to form the trained machine learning model 800. The trained machine learning model 800 is able to perform tasks without explicitly being programmed by making inferences based on patterns found in the analysis of data.

[0082] FIG. 9 depicts an example of a portion of the biopsy needle 1 of FIG. 1 and FIG. 2. The needle portion 900 includes a cylindrical sheath 902, such as the cylindrical sheath 7 of FIG. 2, a needle tube 904, such as the needle tube 3 of FIG. 2, and a needle ramp 906. As seen in FIG. 9, the needle ramp 906 may be extended outwardly from the cylindrical sheath 902, such as at an angle relative to a longitudinal axis of the cylindrical sheath 902.

[0083] As a clinician maneuvers the biopsy needle 1 through the patient anatomy, the needle tube 904 and needle ramp 906 remain positioned within a working channel of the cylindrical sheath 902, which is located proximal to the needle ramp 906. Once the clinician has maneuvered the biopsy needle 1 to the site of interest, the clinician begins advancing needle ramp 906 outwardly from the cylindrical sheath 902. Once the needle ramp 906 is extended, the clinician may advance the needle tube 904 through the working channel and outwardly through the needle ramp 906 and into a biological substance, such as tissue, such that a portion of the length of the needle tube 904 extends beyond a distal end of the needle ramp 906. The present inventor has recognized the desirability of estimating an extension length of the needle tube 904.

[0084] The present inventor has recognized that the needle tube 904 may experience various pressures as it is being advanced outwardly from the cylindrical sheath 902, through the needle ramp 906, and into the biological substance, e.g., tissue. For example, a pressure sensor associated with the needle, e.g., the needle tube 904, may experience a first pressure as the clinician extends the needle through a working channel of the 902, a second pressure as the needle is further extended through the needle ramp 906, and a third pressure as the needle is still further extended into the biological substance. The pressures may be sensed by one or more optical fiber sensors, such as the optical fiber sensor 308 of FIG. 3. As an example, the first pressure experienced by the needle (in the working channel) may be the lowest, the second pressure experienced by the needle (in the needle ramp 906) may be the highest, and the third pressure experienced by the needle (in the biological substance) may be less than the second pressure but higher than the first pressure. In some examples, different biological substances may impart different pressures on the sensor, such as healthy tissue versus cancerous tissue.

[0085] It will be appreciated that this pattern of varying pressure signals may be indicative of the presence of a biological substance within the biopsy needle 1. The first pressure experienced by the biopsy needle 1 known to not have a biological substance is treated as a baseline, the second pressure experienced by the needle is indicative of the needle extending through the needle ramp 906, and the third pressure experienced by the needle being generally indicative of presence of a soft tissue-type sample within the lumen and exerting pressure on the pressure sensors.

[0086] A machine learning model, such as the Al model 704 of FIG. 7, may be trained using techniques similar to those described with respect to the trained machine learning model 800 of FIG. 8. The machine learning model may be trained using pressure data, needle extension data, tissue type data, and the like. Using various techniques of this disclosure, a processor, such as the processor 318 of FIG. 3, may determine a pressure using at least one of the plurality of sensors. Then, the processor may estimate, based on the determined pressure, an extension length of the needle, such as the needle tube 904 of FIG. 9. For example, the processor may use a trained machine learning model to generate an estimate, based on the determined pressure, an extension length of the needle. The processor may then display a representation of the estimate, e.g., numerical, graphical, or some other representation, on a user interface, such as the monitor 116 of FIG. 1.

[0087] FIG. 10 is a flowchart of an example of a method 1000 for sampling biological substances using a biopsy needle. The needle, such as the needle 1 of FIG. 1, defines a lumen and has a longitudinal axis, where the lumen is configured to receive biological substance.

[0088] At block 1002, the method 1000 includes receiving sensor data from a sensor positioned along the longitudinal axis of the needle, such as the sensor 204 in FIG. 4, FIG. 5, and / or FIG. 6.

[0089] At block 1004, the method 1000 includes detecting, based on the sensor data, a presence of the biological substance in the lumen.

[0090] In some examples, the method 1000 includes determining, based on the sensor data, a characteristic of the biological substance; and determining, based on the determined characteristic, a type of the biological substance.

[0091] In some examples, the method 1000 includes detecting, based on the sensor data, a change in a type of biological substance, and displaying, on the user interface, an indication to a user to adjust a stylet positioned within the lumen of the needle so as to permit the biological substance to enter the lumen.

[0092] In some examples, the method 1000 includes detecting, based on first sensor data, the presence of the biological substance in the lumen during an advancement of the needle, and detecting, based on second sensor data, the presence of the biological substance in the lumen during a retraction of the needle.

[0093] In some examples, the method 1000 includes determining a characteristic of the biological substance in the lumen based on the sensor data, and determining, based on the determined characteristic, a type of the biological substance.

[0094] In some examples, the method 1000 includes determining a pressure using at least one of the plurality of sensors, and estimating, based on the determined pressure, an extension length of the needle.

[0095] FIG. 11 illustrates a block diagram of an example of a machine 1100 upon which any one or more of the techniques (e.g., methodologies) discussed herein may perform. Examples, as described herein, may include, or may operate by, logic or a number of components, or mechanisms in the machine 1100. Circuitry (e.g., processing circuitry) is a collection of circuits implemented in tangible entities of the machine 1100 that include hardware (e.g., simple circuits, gates, logic, etc.). Circuitry membership may be flexible over time. Circuitries include members that may, alone or in combination, perform specified operations when operating.

[0096] In an example, hardware of the circuitry may be immutably designed to carry out a specific operation (e.g., hardwired). In an example, the hardware of the circuitry may include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.) including a machine-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.

[0097] The instructions enable embedded hardware (e.g., the execution units or a loading mechanism) to create members of the circuitry in hardware via the variable connections to carry out portions of the specific operation when in operation. Accordingly, in an example, the machine-readable medium elements are part of the circuitry or are communicatively coupled to the other components of the circuitry 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 circuitry. For example, under operation, execution units may be used in a first circuit of a first circuitry at one point in time and reused by a second circuit in the firstcircuitry, or by a third circuit in a second circuitry at a different time. Additional examples of these components with respect to the machine 1100 follow.

[0098] In alternative examples, the machine 1100 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine 1100 may operate in the capacity of a server machine, a client machine, or both in server-client network environments. In an example, the machine 1100 may act as a peer machine in peer-to-peer (P2P) (or other distributed) network environment. The machine 1100 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.

[0099] The machine 1100 may include a hardware processor 1102 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 1104, a static memory (e.g., memory or storage for firmware, microcode, a basic-input-output (BIOS), and mass storage 1108 (e.g., hard drives, tape drives, flash storage, or other block devices) some or all of which may communicate with each other via an interlink 530 (e.g., bus). The machine 1100 may further include a display unit 1110, an alphanumeric input device 1112 (e.g., a keyboard), and a user interface (UI) navigation device 1114 (e.g., a mouse). In an example, the display unit 1110, input device 1112 and UI navigation device 1114 may be a touch screen display. The machine 1100 may additionally include a signal generation device 1118 (e.g., a speaker), a network interface device 1120, and one or more sensors 1116, such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor. The machine 1100 may include an output controller 1128, 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.).

[0100] Registers of the processor 1102, the main memory 1104, the static memory 1106, or the mass storage 1108 may be, or include, a machine readable medium 1122 on which is stored one or more sets of data structures or instructions 1124 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions1124 may also reside, completely or at least partially, within any of registers of the processor 1102, the main memory 1104, the static memory 1106, or the mass storage 1108 during execution thereof by the machine 1100. In an example, one or any combination of the hardware processor 1102, the main memory 1104, the static memory 1106, or the mass storage 1108 may constitute the machine readable media 1122. While the machine readable medium 1122 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 1124.

[0101] The term “machine readable medium” may include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine 1100 and that cause the machine 1100 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. Non-limiting machine readable medium examples may include solid-state memories, optical media, magnetic media, and signals (e.g., radio frequency signals, other photon based signals, sound signals, etc.). In an example, a non-transitory machine readable medium comprises a machine readable medium with a plurality of particles having invariant (e.g., rest) mass, and thus are compositions of matter. Accordingly, non-transitory machine- readable media are machine readable media that do not include transitory propagating signals. Specific examples of non-transitory machine readable media may include: nonvolatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0102] In an example, information stored or otherwise provided on the machine readable medium 1122 may be representative of the instructions 1124, such as instructions 1124 themselves or a format from which the instructions 1124 may be derived. This format from which the instructions 1124 may be derived may include source code, encoded instructions (e.g., in compressed or encrypted form), packaged instructions (e.g., split into multiple packages), or the like. The information representative of the instructions 1124 in the machine readable medium 1122 may be processed by processing circuitry into the instructions to implement any of the operations discussed herein. For example, deriving the instructions 1124 from the information (e.g., processing by the processing circuitry) may include: compiling (e.g., from source code, object code, etc.), interpreting, loading, organizing (e.g., dynamically or statically linking), encoding, decoding, encrypting,unencrypting, packaging, unpackaging, or otherwise manipulating the information into the instructions 1124.

[0103] In an example, the derivation of the instructions 1124 may include assembly, compilation, or interpretation of the information (e.g., by the processing circuitry) to create the instructions 1124 from some intermediate or preprocessed format provided by the machine readable medium 1122. The information, when provided in multiple parts, may be combined, unpacked, and modified to create the instructions 1124. For example, the information may be in multiple compressed source code packages (or object code, or binary executable code, etc.) on one or several remote servers. The source code packages may be encrypted when in transit over a network and decrypted, uncompressed, assembled (e.g., linked) if necessary, and compiled or interpreted (e.g., into a library, stand-alone executable etc.) at a local machine, and executed by the local machine.

[0104] The instructions 1124 may be further transmitted or received over a communications network 1126 using a transmission medium via the network interface device 1120 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.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), LoRa / LoRaWAN, or satellite communication networks, mobile telephone networks (e.g., cellular networks such as those complying with 3G, 4G LTE / LTE-A, or 5G standards), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 1102.11 family of standards known as Wi-Fi®, IEEE 1102.15.4 family of standards, peer-to-peer (P2P) networks, among others.

[0105] In an example, the network interface device 1120 may include one or more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more antennas to connect to the communications network 1126. In an example, the network interface device 1120 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 singleoutput (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 1100, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software. A transmission medium is a machine-readable medium.Various Notes

[0106] Each of the non-limiting claims or examples described herein may stand on its own, or may be combined in various permutations or combinations with one or more of the other examples.

[0107] 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 may be practiced. These embodiments are also referred to herein as “examples.” Such examples may 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 claims thereof), either with respect to a particular example (or one or more claims thereof), or with respect to other examples (or one or more claims thereof) shown or described herein.

[0108] In the event of inconsistent usages between this document and any documents so incorporated by reference, the usage in this document controls.

[0109] 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.

[0110] Method examples described herein may be machine or computer-implemented at least in part. Some examples may include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods may include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code may include computer readable instructions for performing various methods. The code may form portions of computer program products. Further, in anexample, the code may be tangibly stored on one or more volatile, non-transitory, or nonvolatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media may include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact discs and digital video discs), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.[OHl] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more claims thereof) may be used in combination with each other. Other embodiments may 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 may 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

CLAIMSWhat is claimed is:

1. A biological substance sampling system, comprising: a needle defining a lumen and a having a longitudinal axis, wherein the lumen is configured to receive a biological substance; a sensor positioned along the longitudinal axis of the needle, the sensor configured for generating sensor data; and a processor configured for receiving the sensor data and detecting, based on the sensor data, a presence of the biological substance in the lumen.

2. The biological substance sampling system of claim 1, wherein the sensor includes a fiber Bragg grating sensor.

3. The biological substance sampling system of claim 1, wherein the sensor is positioned to sense an environmental characteristic within the lumen.

4. The biological substance sampling system of claim 1, wherein the sensor is positioned to sense an environmental characteristic at an exterior surface of the needle.

5. The biological substance sampling system of claim 1, wherein the sensor includes a first sensor and a second sensor, and wherein the first sensor is positioned to sense an environmental characteristic at an exterior surface of the needle.

6. The biological substance sampling system of claim 5, wherein the processor is further configured for: determining, based on the sensor data from the first sensor, a characteristic of the biological substance; and determining, based on the determined characteristic, a type of the biological substance.

7. The biological substance sampling system of claim 5, wherein the biological substance sampling system further includes a user interface having a display, and wherein the processor is configured for: detecting, based on the sensor data, a change in a type of biological substance; and displaying, on the user interface, an indication to a user to adjust a stylet positioned within the lumen of the needle so as to permit the biological substance to enter the lumen.

8. The biological substance sampling system of claim 1, wherein the processor configured for detecting, based on the sensor data, the presence of the biological substance in the lumen is configured for: detecting, based on first sensor data, the presence of the biological substance in the lumen during an advancement of the needle; and detecting, based on second sensor data, the presence of the biological substance in the lumen during a retraction of the needle.

9. The biological substance sampling system of claim 1, wherein the sensor is positioned within the lumen, and wherein the processor is further configured for: determining a characteristic of the biological substance in the lumen based on the sensor data; and determining, based on the determined characteristic, a type of the biological substance.

10. The biological substance sampling system of claim 1, wherein the sensor includes at least one of a pressure sensor and a temperature sensor.

11. The biological substance sampling system of claim 1, wherein the sensor includes a plurality of sensors that are longitudinally offset from one another along the longitudinal axis.

12. The biological substance sampling system of claim 11, wherein the plurality of sensors includes pressure sensors, and wherein the processor is configured for: determining a pressure using at least one of the plurality of sensors; and estimating, based on the determined pressure, an extension length of the needle.

13. A method for sampling biological substances using a needle defining a lumen and a having a longitudinal axis, wherein the lumen is configured to receive biological substance, the method comprising: receiving sensor data from a sensor positioned along the longitudinal axis of the needle; and detecting, based on the sensor data, a presence of the biological substance in the lumen.

14. The method of claim 13, comprising: determining, based on the sensor data, a characteristic of the biological substance; anddetermining, based on the determined characteristic, a type of the biological substance.

15. The method of claim 13, comprising: detecting, based on the sensor data, a change in a type of biological substance; and displaying, on the user interface, an indication to a user to adjust a stylet positioned within the lumen of the needle so as to permit the biological substance to enter the lumen.

16. The method of claim 13, comprising: detecting, based on first sensor data, the presence of the biological substance in the lumen during an advancement of the needle; and detecting, based on second sensor data, the presence of the biological substance in the lumen during a retraction of the needle.

17. The method of claim 13, comprising: determining a pressure using at least one of the plurality of sensors; and estimating, based on the determined pressure, an extension length of the needle.

18. A biological substance sampling system, comprising: a needle defining a lumen and a having a longitudinal axis, wherein the lumen is configured to receive a biological substance; a fiber Bragg grating sensor positioned along the longitudinal axis of the needle, the fiber Bragg grating sensor configured for generating pressure sensor data; and a processor configured for receiving the pressure sensor data and detecting, based on the pressure sensor data, a presence of the biological substance in the lumen.

19. The biological substance sampling system of claim 18, wherein the fiber Bragg grating sensor includes a plurality of sensors that are longitudinally offset from one another along the longitudinal axis.

20. The biological substance sampling system of claim 18, wherein the processor configured for detecting, based on the pressure sensor data, the presence of the biological substance in the lumen is configured for: detecting, based on first pressure sensor data, the presence of the biological substance in the lumen during an advancement of the needle; and detecting, based on second pressure sensor data, the presence of the biological substance in the lumen during a retraction of the needle.

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