Devices, systems, and methods for determining inflammation and / or fibrosis

Abstract

A method for assessing a gastrointestinal tract of a subject may include using a sensor located in the subject to obtain data regarding tissue quality of the gastrointestinal tract; using the obtained data, blood perfusion measurements in the tissue are determined. Determining a thickness measurement of the tissue using the obtained data; determining an inflammation measurement of the tissue using the perfusion measurement and the thickness measurement; and classifying the state of the tissue using one or more of the perfusion measurement, the thickness measurement, and the inflammation measurement.

Description

This case is a divisional application of invention patent application number 201980057704.3 Cross-references to related literature

[0001] This application claims priority to U.S. Provisional Patent Application No. 62 / 694,163, filed on July 5, 2018, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0002] The present disclosure generally relates to devices, systems and / or methods for diagnosing and / or monitoring disease by, for example, determining inflammation and / or fibrosis. More specifically, various aspects of the present disclosure relate to devices, systems and / or methods for scoring inflammation and determining fibrosis using indices of perfusion and tissue thickening. Background Art

[0003] Inflammatory bowel disease (“IBD”) is a chronic disease characterized by chronic inflammation of the gastrointestinal (“GI”) tract. The disease affects 5-6 million people worldwide, with approximately 1.6 million patients in the United States. Patients and healthcare providers can spend a significant amount of money each year treating IBD, with direct costs estimated to be between $11 billion and $28 billion per year in the United States. Additionally, patients with IBD may experience longer hospital stays, higher costs, and higher readmission rates than patients without IBD.

[0004] There are two main types of IBD. Ulcerative colitis ("UC") and Crohn's disease ("CD"). CD can affect any part of the digestive system and is characterized by cross-mural involvement. Symptoms of CD include abdominal pain, fever, cramping, rectal bleeding, and frequent diarrhea. The peak age of onset of CD disease is between 15-35 years old. UC affects only the colon, with mucosal involvement. UC has mild to severe symptoms that are similar to those of CD. UC may have fewer complications than CD. Colectomy may be used to treat UC. The peak age of onset of UC is between 15-30 years old and 50-70 years old. 55% of people with IBD have UC, and 45% of people with IBD have CD.

[0005] A patient's IBD may become more severe over time. For example, in the early stages of the disease, IBD may affect the mucosal lining of the small intestine and / or colon. Over time, IBD may progress and affect the entire intestinal and / or colon wall. On the other hand, treatment of IBD may result in symptom relief. Traditional treatments focus on addressing current symptoms. However, traditional treatment approaches lack the ability to robustly assess GI tract inflammation and the presence of fibrosis. Technologies such as MRI have limited resolution and cannot provide real-time information. Color-enhanced ultrasound equipment is not standardized, is often unavailable, and adds significant costs. For example, color-enhanced ultrasound may not be performed in an endoscopy suite. Without information about the properties of fibrosis in the GI tract, physicians' ability to effectively treat the disease may be limited. Summary of the invention

[0006] Examples of the present disclosure relate to devices, systems and / or methods, etc., for diagnosing and / or monitoring disease by, for example, determining inflammation and / or fibrosis. Each example disclosed herein may include one or more features related to any other disclosed example.

[0007] A method for assessing the gastrointestinal tract of a subject may include using data regarding the quality of tissue of the gastrointestinal tract to determine a measurement of blood perfusion in the tissue; using this data, determining a measurement of thickness of the tissue; using the perfusion measurement and the thickness measurement, determining a measurement of inflammation of the tissue; and using one or more of the perfusion measurement, the thickness measurement, and the inflammation measurement, to classify the state of the tissue.

[0008] Any method described herein may include one or more features or steps described below. Classifying the state of a tissue may include classifying a fibrotic property of the tissue. Classifying the fibrotic property may include determining whether the tissue is fibrotic. The perfusion measurement may be a perfusion index. The perfusion index may be a ratio of the amount of pulsating light to the amount of total light. Determining the inflammation measurement may include: applying a first function to the perfusion measurement; and applying a second function to the thickness measurement. Determining the inflammation measurement may further include: adding the result of the first function and the result of the second function. Applying the first function may include applying a weighted value. Applying the first function may include applying a sigmoid function. Applying the second function may include applying a sigmoid function. Applying the first function may include calculating the difference between the perfusion measurement and a reference measurement of perfusion. Using one or more of the perfusion measurement, the thickness measurement, and the inflammation measurement may include using at least two of the perfusion measurement, the thickness measurement, and the inflammation measurement. Using one or more of the perfusion measurement, the thickness measurement, and the inflammation measurement may include using each of the perfusion measurement, the thickness measurement, and the inflammation measurement. Using the perfusion measurement, thickness measurement, and inflammation measurement may include classifying the perfusion measurement and classifying the inflammation measurement. The method may further include comparing the inflammation measurement to a reference measurement of inflammation. The reference measurement may represent the amount of inflammation in healthy tissue.

[0009] A method for evaluating the gastrointestinal tract of a subject may include using a sensor located in the subject to obtain data regarding tissue quality of the gastrointestinal tract; using the obtained data, determining a blood perfusion measurement in the tissue; using the obtained data, determining a thickness measurement of the tissue; using the perfusion measurement and the thickness measurement, determining an inflammation measurement of the tissue; and using one or more of the perfusion measurement, the thickness measurement, and the inflammation measurement, classifying a state of the tissue.

[0010] Any method described herein may include one or more features or steps described below. Classifying the state of the tissue may include classifying the fibrotic properties of the tissue. The sensor may be an optical sensor located in the gastrointestinal tract lumen. The perfusion measurement may be a perfusion index. The perfusion index may be a ratio of the amount of pulsed light received by the sensor to the total amount of light received by the sensor. Determining the inflammation measurement may include applying a first function to the perfusion measurement; and applying a second function to the thickness measurement. Determining the inflammation measurement may include adding the result of the first function and the result of the second function. Applying the first function may include applying a weighted value. Applying the first function may include applying a sigmoid function. Applying the first function may include calculating the difference between the perfusion measurement and a reference measurement of perfusion. Using one or more of the perfusion measurement, the thickness measurement, and the inflammation measurement may include using each of the perfusion measurement, the thickness measurement, and the inflammation measurement. Using the perfusion measurement, the thickness measurement, and the inflammation measurement may include classifying the perfusion measurement and classifying the inflammation measurement. The method may further include comparing the inflammation measurement with a reference measurement of inflammation.

[0011] In another example, a method for evaluating the gastrointestinal tract may include using a sensor located within the lumen of the gastrointestinal tract to obtain data about the quality of gastrointestinal tissue; using the obtained data, determining a blood perfusion measurement in the tissue; using the obtained data, determining a thickness measurement of the tissue; using the perfusion measurement and the thickness measurement, determining an inflammation measurement; and using the inflammation measurement, characterizing the fibrotic properties of the tissue. Any method described herein may include one or more of the features or steps described below. The sensor may be an optical sensor. The perfusion measurement may be a perfusion index. The perfusion index may be a ratio of the amount of pulsed light received by the sensor to the total amount of light received by the sensor. Determining the inflammation measurement may include applying a first function to the perfusion measurement; and applying a second function to the thickness measurement. Determining the inflammation measurement may further include adding the result of the first function and the result of the second function. Applying the first function may include applying a weighted value. Applying the first function may include applying a sigmoid function.

[0012] In another example, a system for evaluating the gastrointestinal tract may include: a sensor configured to be placed in the gastrointestinal cavity of a patient to acquire data about gastrointestinal tissue; and a processor configured to: determine a blood perfusion measurement in the tissue using the acquired data; determine a tissue thickness measurement using the acquired data; determine a tissue inflammation measurement using the perfusion measurement and the thickness measurement; and classify a state of the tissue using one or more of the perfusion measurement, the thickness measurement, and the inflammation measurement.

[0013] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not limiting of the claimed invention. As used herein, the terms "comprises," "comprising," or any other variation thereof, are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements does not include only these elements, but may include other elements not expressly listed or inherent to the process, method, article, or apparatus. The term "exemplary" is used in the sense of "example" rather than "ideal." BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate examples of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0015] Figure 1 Exemplary systems for scoring inflammation and determining fibrosis are described.

[0016] Figure 2-3 Exemplary systems for scoring inflammation and determining fibrosis are described.

[0017] Figure 4A-4B An exemplary user interface for displaying information about a disease state is described. DETAILED DESCRIPTION

[0018] The present disclosure is directed to the equipment, system and / or method for diagnosing and / or monitoring diseases by, for example, determining inflammation and / or fibrosis. Specifically, in at least some aspects, equipment, system and / or method can be used for scoring inflammation and determining fibrosis using the index of perfusion and tissue thickening. Equipment, system and method for diagnosing and / or monitoring diseases by, for example, scoring inflammation and determining fibrosis as described herein can also be used for monitoring other diseases, including, for example, other GI diseases such as irritable bowel syndrome, gastritis, gastroesophageal reflux disease (GERD), Barrett's esophagus, polyps, colorectal cancer, peptic ulcer, dysphagia, cholecystitis, diverticular disease, celiac disease, and diseases such as chronic asthma, chronic bronchitis, emphysema, gastritis, esophagitis, duodenal ulcer, gastroenteritis. Although IBD monitoring or assessment is mentioned herein, mentioning IBD should not be interpreted as limiting the application of the disclosed system. Although the present disclosure is directed to identification / diagnosis of fibrosis and / or inflammation, various aspects of the present disclosure can also be used for diagnosing any of the above conditions or the impact caused by the above conditions.

[0019] Figure 1An exemplary system 10 for scoring inflammation and determining fibrosis is described. The system 10 may include one or more software applications 12. The software application 12 may interact with, for example, a medical device 14, a user interface 16, and a memory 18. The software application 12 may also interact with other software applications and / or other components, including sensors, medical records, treatment delivery systems, environmental data / sensors, medical staff input, and / or user input.

[0020] Medical device 14 may include an imager capable of capturing images, including optical, infrared, thermal or other images. Medical device 14 may also include other types of sensors or inputs, including optical sensors, ultrasonic sensors and / or photodiodes. Medical device 14 may be capable of capturing still images, video images, or both still images and video images. Medical device 14 may be configured to transmit images or other data to a receiving device via a wired or wireless connection. Medical device 14 may be, for example, a component of an endoscopic system, a component of a tool deployed in a working port of an endoscope, a wireless endoscopic capsule, or one or more implanted monitors or other devices. In the case of an implanted monitor, such an implanted monitor may be a permanent or temporary implant. Examples of medical devices applicable to the present disclosure are described in U.S. Patent Application No. 16 / 011 / 925 filed on June 19, 2018, the complete disclosure of which is incorporated herein by reference.

[0021] The medical device 14 may be configured to capture data at one or more locations of the GI tract, including the esophagus, stomach, duodenum, small intestine, and / or colon. For example, during an endoscopic procedure, a medical device 14 carried on an endoscope or a tool deployed in an endoscope (or simply the endoscope itself if equipped with a suitable imaging component) may be advanced through various portions of the GI tract and may capture images or other data at a given portion or at a number of locations across a portion. The medical device 14, a device carrying the medical device 14, or another component of the system 10, such as the software application 12, may be able to determine the location of the GI tract at which data is captured. For example, the medical device 14, a device carrying the medical device 14, or another component of the system 10, such as the software application 12, may be able to determine whether an image or other data is captured in the esophagus, stomach, ileum, right colon, transverse colon, left colon, rectum, or jejunum, and in some cases, determine the precise location of such organs. The medical device 14 carried by the wireless endoscopic capsule may capture data at different points as it passes through the gastrointestinal tract. The medical device 14 as part of the implantable monitor can be fixed in one location, or can capture data at multiple locations. For example, an implantable monitor can include many medical devices 14 at different locations within the GI tract. The medical device 14 can capture data continuously or periodically.

[0022] The medical device 14 may be configured to capture data about blood perfusion into the GI tract tissue. For example, the medical device 14 may be configured to capture data about the penetration of blood into the tissue. In order to capture the perfusion data, the medical device 14 may include a light source and a receiver. The medical device 14 may include, for example, an optical sensor. For example, the medical device 14 may include an LED light source and a photodiode receiver. In an alternative, the medical device 14 may include an alternative light source, such as a light guide, an optical fiber, or other light source, such as a laser pulsed at a specific wavelength (e.g., 530nm and / or 930nm) for non-invasive photoacoustic excitation through the abdominal cavity. The medical device 14 may also include an alternative receiver, such as an acoustic transducer receiver. The medical device 14 may capture information about the absorption and reflection of light waves in the region of interest in the GI tract. The captured information may include, for example, data indicating the amount of pulsed light (e.g., varying light) received by the sensor of the medical device 14. The sensor of the medical device 14 may capture information about the AC component of the captured light data, which may indicate the amount of pulsating light measured. The captured information may also include, for example, data indicating the total amount of light received. For example, a sensor of the medical device 14 may capture information regarding a DC component of the captured light data, which may be indicative of the total amount of light measured.

[0023] The medical device 14 can also capture data related to tissue thickness. Tissue thickening can be the result of increased blood flow to the tissue and occlusion of blood vessels and / or inflammation. The medical device 14 can include components configured to capture information related to intestinal wall thickness. For example, the components of the medical device 14 can include spaced light sources and thus can have spatial diversity of light sources. The receiver can measure the amount of light reflected from the tissue. For each spatially different emitter, the different absorbance in the tissue starting from the excitation point can have a linear relationship with the tissue thickness. Given a set of fixed distances between the light source and the receiver, different depths of the tissue can provide different slopes of the linear relationship.

[0024] An implantable device, including one or more medical devices 14, may be delivered during a colonoscopy via a natural orifice endoscopic surgery (NOTES) procedure. For example, during a colonoscopy, an incision may be made and a sensor may be implanted outside the cavity on the omentum. Using such a procedure may provide benefits, including using an endoscopic suite rather than an operating room. If the medical device 14 requires batteries, the batteries may be replaced during a routine colonoscopy. The medical device 14 may also be delivered via laparoscopic surgery or a different surgical or non-surgical procedure.

[0025] The medical device 14 can communicate directly or indirectly with the software application 12, which can be stored on a processor or other suitable hardware. The medical device 14 can be connected to the software application 12 via a wired or wireless connection. In an alternative, the medical device 14 can communicate with another type of processing unit. The software application 12 can run on a dedicated device, a general-purpose smartphone or other portable device, and / or a personal computer. The software application 12 can also be part of an endoscopic system, an endoscopic tool, a wireless endoscopic capsule, or an implantable device that also includes the medical device 14. The software application 12 can be connected to the user interface 16 and / or the memory 18 via a wired or wireless connection.

[0026] The software application 12 or any other type of processing unit can analyze signals received from the medical device 14 and other inputs, and can extract information from data obtained from the medical device 14 and other inputs. The software application 12 or any other suitable component of the system 10 can apply algorithms to the signals or data from the medical device 14 and other inputs.

[0027] The software application 12 may store information about algorithms, imaging data, or other data in the memory 18. Data such as input from the medical device 14 may be stored locally by the software application 12 in the memory 18 on a dedicated device or a general-purpose device such as a smart phone or computer. The memory 18 may be used for short-term storage of information. For example, the memory 18 may be RAM memory. The memory 18 may additionally or alternatively be used for long-term storage of information. For example, the memory 18 may be flash memory or solid-state memory. In an alternative, data from the medical device 14 may be stored remotely in the memory 18 by the software application 12, such as in a cloud-based computing system.

[0028] The user interface 16 may allow a user, such as a medical professional, patient, or other user, to access information stored in the memory 18 or other information generated by the software application 12. The user interface 16 may allow a user to view and input data through a terminal, handheld device, mobile device, desktop device, or any other suitable device. The user interface 16 may facilitate interaction by multiple users simultaneously or sequentially. The user interface 16 may provide text, graphics, video, audio, or other output.

[0029] As described above, the software application 12 can be configured to analyze data captured by the medical device 14 related to, for example, tissue perfusion and / or tissue thickness. For example, the software application 12 can be configured to apply an algorithm to the data captured by the medical device 14. The algorithm can be stored in the memory 18. The software application 12 can store the results or any components of its analysis in the memory 18. The memory 18 can then be used, for example, to track disease progression over time. The software application 12 can perform a number of analyses and generate a variety of graphs or other data.

[0030] Figure 2 A method 100 for evaluating characteristics of the GI tract is shown. More specifically, Figure 2 A method 100 for measuring, evaluating, and displaying fibrotic properties of the GI tract is shown. One or more steps of method 100 may be omitted and / or additional steps may be added to method 100. Any method resulting from omitted and / or additional steps is within the scope of the present disclosure. In step 102, the above-described Figure 1 A system such as system 10 described may acquire reference data from healthy tissue of the GI tract. For example, the medical device 14 of system 10 may be used to acquire data that may, for example, indicate the amount of light absorbed and / or reflected by healthy tissue and may be collected from, for example, a photodiode and / or an optical array. The data acquired in step 102 may be from a subject for whom the fibrotic properties of the GI tract need to be characterized. In an alternative, the data acquired in step 102 may be from a different subject, such as a healthy subject. In another alternative, the data acquired in step 102 may be data based on multiple subjects. For example, the data acquired in step 102 may be acquired from a patient database.

[0031] In step 104, a component of the system 10, such as the software application 12, may characterize the perfusion and / or tissue thickness of healthy tissue based on the data acquired in step 102. To characterize the perfusion, the software application 12 may determine a perfusion index for the tissue sample. The perfusion index may be a measurement of blood perfusion into the GI tract tissue. The perfusion index may be determined using data collected by the medical device 14. For example, the data may be collected by a receiver (e.g., an optical receiver and / or a photodiode) of the medical device 14. The perfusion index may include a ratio of the amount of pulsating light (which may be an AC component) measured by the medical device 14 to the amount of total light (which may be a DC component) measured by the medical device 14. For example, the perfusion index may be calculated according to the following formula:

[0032] A perfusion index of less than 2% may indicate low perfusion. A perfusion index between 2% and 4% may indicate moderate perfusion. A perfusion index between 4% and 8% may indicate adequate perfusion. A perfusion index greater than 8% may indicate excessive perfusion. The above formulas are merely exemplary. Any other suitable method may be used to calculate the amount of perfusion in GI tract tissue. The above ranges are also merely exemplary. These ranges may vary based on the patient, location, measurement tool, formula used, or other factors.

[0033] The software application 12 can also use data from the medical device 14 to characterize the thickness of the GI tract tissue. For example, the software application 12 can characterize the thickness measurement of the intestinal wall. The thickness can be characterized as a thickness score and can be expressed as a percentage value or a length unit (e.g., millimeters). For a healthy subject, the jejunum or ileum can have a thickness of about 1.1mm-2mm, the sigmoid colon can have a thickness of about 1.4mm-3mm, and the ascending colon can have a thickness of about 1.1mm-2.5mm. The increase in tissue thickness can be evaluated in an area of ​​interest that shows visible signs of inflammation, such as redness, ulcers, "cobblestone-like" and / or stenosis. The increase in tissue thickness can be measured in comparison to a baseline measurement previously made for the subject or from a measurement made in a healthy area near the affected area. The difference in tissue thickness can be evaluated, and the affected tissue can be shown to be 2-4 times thicker than the healthy area or the baseline area. The thickness score or the change in the thickness score can tend to a sigmoid function.

[0034] In step 106, the software application 12 may characterize inflammation of the healthy tissue sample using the data collected and / or characterized in steps 102 and 104. For example, the software application 12 may determine a measure of inflammation, which may be a reference inflammation score. The software application 12 may apply an operator or function to the thickness score and perfusion index calculated in step 104, weight the results, and add the resulting values. For example, the software application 12 may apply a sigmoid function to a characterization of perfusion and / or thickness, such as those described above. The sigmoid function applied in step 106 may be a special case of a logistic function defined by the following equation, where S(x) is a sigmoid function and e represents the mathematical constant "e".

[0035] The software application 12 may calculate an inflammation score in step 106. The inflammation score ("I," in the formula below) may represent the amount of inflammation that has occurred in the tissue due to a disease (e.g., the diseases described above), and may take into account both increased perfusion and increased tissue thickness. To calculate the inflammation score, a sigmoid function may be applied to the logarithm of the perfusion index and / or to the logarithm of the thickness. The sigmoid functions applied to the logarithm of the perfusion index and the logarithm of the thickness may be weighted. For example, the sigmoid for the logarithm of the perfusion index may have a weighting factor of 1 / 3 applied, and the sigmoid for the logarithm of the thickness may have a weighting factor of 2 / 3 applied. For example, the inflammation score may be calculated according to the following formula: l={C 1 S(log(P))+C 2 S(log(T))}×100% Where I is the inflammation score (expressed as a percentage), C 1 is the first constant, C 2 is the second constant, S(x) is the sigmoid function, P is the perfusion index, and T is the thickness score. The perfusion index (P) may be divided by 100 before inserting it into the above inflammation score formula. C in any of the formulas herein 1 It can be equal to 1 / 3 or equal to 0.33. C in any formula in this article 2 It can be equal to 2 / 3 or equal to 0.67. Constant C 1 and C 2 These values ​​are merely exemplary. The constant may have any suitable value, which may be determined in any suitable manner. For example, the constant may be determined empirically based on a particular patient, a subset of patients, or the population as a whole. Expanding on the above formula, the inflammation score may be calculated according to the following formula:

[0036] Any of the above systems or methods can also be used to aggregate patient data. The above systems or methods can compare information collected from a particular patient with data collected from other patients and / or manually entered data regarding patient classification. For example, any of the above systems or methods can use a library of conditions. The above systems and methods can be used to stratify patients based on their risk of an exacerbation of their disease state. This stratification can be based on data previously collected from a particular patient, or based on data for a particular patient population or an entire patient population.

[0037] In step 108, as described above with respect to Figure 1The system 10 described herein can acquire sample data from tissue of the GI tract. For example, the medical device 14 of the system 10 can be used to acquire such data. The data acquired in step 108 can have any characteristics of the data acquired in step 102. For example, the data acquired in step 108 can indicate the amount of light absorbed and / or reflected by the tissue. The data acquired in step 108 can relate to tissue suspected of having fibrosis or tissue that needs to be evaluated for fibrosis. The data acquired in step 108 can be from the same subject as the data acquired in step 102, or from a different subject.

[0038] In step 110, a component of system 10, such as software application 12, may characterize perfusion and / or tissue thickness of the sample tissue based on the data acquired in step 108. Step 110 may use any of the techniques described with respect to step 104, or an alternative step may be used.

[0039] In step 112, the software application 12 may characterize inflammation of the sample tissue using the data collected and / or characterized in step 108 and / or step 110. For example, the software application 12 may determine an inflammation score for the sample tissue. Step 112 may use any of the techniques discussed above with respect to step 106, or may use additional or alternative techniques.

[0040] In step 114, the software application 12 may compare a sample inflammation score, such as calculated in step 112, to a reference inflammation score, such as calculated in step 106. For example, the software application 12 may determine a difference between the sample inflammation score and the reference inflammation score. In an alternative, the software application 12 may determine a ratio of the sample inflammation score to the reference inflammation score. Any other metric may be used to compare the reference inflammation score to the sample inflammation score. In an alternative, step 114 may be omitted.

[0041] In step 116, the software application 12 may determine the fibrotic properties of the sample tissue. In step 116, the software application 12 may consider the results of the earlier steps of method 100. Step 116 may also use any of the techniques described below with respect to step 212 (see Figure 3 ).

[0042] In step 116, the software application 12 may consider a reference perfusion index and / or a reference thickness score (such as those determined in step 104), a reference inflammation score (such as those determined in step 106), a sample perfusion index and / or a sample thickness score (such as those determined in step 110), a sample inflammation score (such as those determined in step 112), and / or a comparison of a reference inflammation score and a sample inflammation score (such as those performed in step 114). For example, a perfusion index of 0.5%-5% and / or a thickness score of 1.2 mm-3 mm may fall into the "healthy" category and may be indicative of healthy tissue (more specific ranges for different portions of the GI tract are discussed above). A perfusion index of 5-10% and / or a thickness score of 3-10 mm may fall into the "fibrotic" category and may be indicative of fibrotic tissue. A perfusion index of less than or equal to 5% or a perfusion index of greater than or equal to 20% and / or a thickness score of 3-14 mm may be classified as an "inflammation" category and may indicate inflammation. An inflammation score of less than or equal to 50% may indicate healthy tissue. An inflammation score of greater than or equal to 50% may indicate fibrotic tissue or inflammation and may indicate unhealthy tissue. Changes in perfusion index and thickness may be classified in a variety of ways. For example, changes in perfusion index or thickness may be mild, moderate, or severe. Mild changes may correspond to the "healthy" category described above. Moderate changes may correspond to the "fibrotic" category described above. Severe changes may correspond to the "inflammation" category described above.

[0043] As an example, the software application 12 may determine whether the inflammation score represents healthy tissue (e.g., an inflammation score less than or equal to 50%) or unhealthy tissue (e.g., an inflammation score greater than 50%). The inflammation score may be used as a composite score, and the base thickness score and perfusion index may only be further reviewed if the inflammation score is greater than 50%.

[0044] If the inflammation score indicates unhealthy tissue, the software application 12 may further analyze the thickness score and / or the perfusion index. If the perfusion index and thickness score increase at approximately the same rate, the tissue may be characterized as having only inflammation, resulting in edema. If the rate of increase in tissue thickness is higher than the rate of increase in perfusion index, the tissue may be classified as fibrotic because more scarring has occurred, resulting in thickening of the tissue. When analyzing the tissue, the software application 12 may apply the above-mentioned categories: "healthy", "fibrotic", and "inflammation". In an alternative, the software application 12 may apply the categories of "mild", "moderate", and "severe" as described above, where the categories increase in the direction of "mild" to "severe". Thickness changes in a higher grade than perfusion changes may indicate fibrotic tissue. On the other hand, thickness changes in a similar grade to perfusion changes may indicate edema and / or inflammation. The above-mentioned values ​​are merely exemplary and not limiting. Other values ​​may be used depending on the specific technique used to determine the perfusion index, thickness score, and / or inflammation score. The values ​​used may also depend on the characteristics of the subject or patient, the instrument used, or other factors.

[0045] In step 118, the results may be displayed on a graphical user interface, such as user interface 16. The results displayed in step 118 may include the results of any of the above steps or any other results. The results displayed in step 118 may be in the form of a table, a graph, a chart, text, an animation, or any other form. Figure 4A-4B An exemplary graphical user interface display is shown.

[0046] Figure 3 An exemplary additional or alternative method 200 for assessing GI tract characteristics is shown. Method 200 may incorporate any of the steps described above with respect to method 100. The steps of method 200 and method 100 may be combined or rearranged in any suitable combination. Certain steps of method 200 and method 100 may also be omitted.

[0047] In step 202, such as the above Figure 1 The system 10 described above can obtain reference data from healthy tissue of the GI tract. In step 202, any of the techniques described above with respect to step 102 can be performed to obtain the reference data. In step 204, a component of the system 10, such as the software application 12, can characterize the perfusion and / or tissue thickness of the healthy tissue based on the data obtained in step 202. Step 204 can utilize any of the techniques described above with respect to step 104. In step 206, any of the techniques described above with respect to step 106 can be performed to obtain the reference data. Figure 1The system 10 described herein can acquire sample data from tissue of the GI tract. Step 206 can implement any of the techniques described above with respect to steps 102, 108, and / or 202. In step 208, a component of the system 10, such as the software application 12, can characterize the perfusion and / or tissue thickness of the sample tissue based on the data acquired in step 206. Step 208 can use any of the techniques described with respect to steps 104, 110, and / or 204, or an alternative step can be used.

[0048] In step 210, the software application 12 may determine one or more inflammation scores. Any of the techniques used in steps 106 and / or 112 may be used. In an alternative, the function applied may determine a change in perfusion index and / or thickness score, rather than the function applied being a sigmoid function. The inflammation score calculated in step 210 may involve calculating a perfusion index difference between a reference perfusion index and / or a reference thickness score characterized in step 204 and a sample perfusion index and / or a sample thickness score characterized in step 208. The difference in thickness score and / or the difference in perfusion index may be weighted according to a weighting factor. The weighting factor used in step 210 may be the same or different than the weighting factor used in steps 106 and / or 112 discussed above. After weighting, the difference in thickness score and / or the difference in perfusion index may be added together to generate an inflammation score. For example, the inflammation score may be calculated according to the following formula: I=C 1 ΔP+C 2 ΔT Where I is the inflammation score (expressed as a percentage), C 1 is the first constant, C 2 is the second constant, P is the perfusion index, and T is the thickness score. 1 It can be equal to 1 / 3 or equal to 0.33. C in any formula in this article 2 It can be equal to 2 / 3 or equal to 0.67. Constant C 1 and C 2 These values ​​are merely exemplary. The constant can have any suitable value and can be determined in any suitable manner. For example, the constant can be determined empirically based on a specific patient, a portion of a patient, or an entire population. An inflammation score of less than or equal to 25% can indicate relief. An inflammation score of 25% to 90% can indicate mild to moderate disease. An inflammation score greater than 90% can indicate moderate to severe disease. The above ranges are merely exemplary. These ranges can vary depending on the patient, location, measurement tool, formula used, or other factors.

[0049] The change in perfusion index, ΔP, can be calculated by subtracting a first perfusion index (e.g., a reference perfusion index determined in step 204) from a second perfusion index (e.g., a sample perfusion index determined in step 208). The result can be divided by the first perfusion index and multiplied by 100 to provide a change in perfusion index in percent. For example, the change in perfusion index can be calculated using the following formula: where ΔP is the change in perfusion index, P n-1 is the first perfusion index, P n is the second perfusion index. A change in the perfusion index of less than or equal to 25% (compared to a healthy reference sample) may indicate remission. A change in the perfusion index of 25% to 90% may indicate mild to moderate disease. A change in the perfusion index of greater than 90% may indicate moderate to severe disease. The above ranges are exemplary only. These ranges may vary based on the patient, location, measurement tool, formula used, or other factors.

[0050] The change in thickness score, ΔT, can be calculated by subtracting a first thickness score (e.g., a reference thickness score determined in step 204) from a second thickness score (e.g., a sample thickness score determined in step 208). The result can be divided by the first thickness score and multiplied by 100 to provide a change in thickness score in percent. For example, the change in thickness score can be calculated using the following formula: Where ΔT is the change in thickness score, T n-1 is the first thickness score, T n is a second thickness score. The thickness score or the change in the thickness score may tend to a sigmoid function. Applying the sigmoid function may limit the score to within 0 to 1 (multiplied by 100%). Applying the sigmoid function may similarly limit the inflammation score comprising the thickness score or the change in the thickness score. A change in the thickness score of less than or equal to 25% (compared to a healthy reference sample) may indicate remission. A change in the thickness score of between 25% and 90% may indicate mild to moderate disease. A change in the thickness score of greater than 90% may indicate moderate to severe disease.

[0051] In step 212, the fibrosis attribute of the tissue sample can be determined or otherwise characterized. The fibrosis attribute can be a measurement of fibrosis in the tissue, and can include a numerical or qualitative value assigned to the fibrosis, and can represent the severity of the fibrosis. In an alternative, the fibrosis attribute can be a "yes" or "no" indicator about the presence of fibrosis. The fibrosis attribute can also be an indicator of a tendency toward or away from fibrosis or the degree of fibrosis. Step 212 can use any of the techniques of step 116 above. In addition or in an alternative, the fibrosis attribute can be characterized using the values ​​determined above, including inflammation scores, changes in thickness scores, and changes in perfusion index. As described above, the inflammation score can be used as a comprehensive score. If the inflammation score falls within a certain range (e.g., above 50%, or above 25%), the changes in thickness scores and / or changes in perfusion are further analyzed. If the inflammation score is within a healthy range, further analysis may not be required.

[0052] In general, if the change in the perfusion index is of a higher severity than the change in the thickness score, the tissue may be labeled as fibrotic. As described above, tissue thickening with increased perfusion may indicate edema and / or inflammation. On the other hand, tissue thickening without increased perfusion may indicate scarring and fibrosis.

[0053] For example, if the inflammation score calculated in step 210 is greater than or equal to 25% (and therefore indicates at least mild disease), but the increase in perfusion is minimal, then the sample tissue can be characterized as fibrotic. Such an inflammation score would indicate a moderate increase in thickness with little or no increase in the perfusion index. Although the total inflammation score may be 25%, at least one of (a) the thickness score and (b) the perfusion index has increased (because the inflammation score is a combination of the change in the thickness score and the change in the perfusion index). If the thickness score has increased, but the increase in perfusion is small, scarring may have occurred. If the inflammation score calculated in step 210 is moderate to severe (i.e., greater than 50%), and the perfusion index is in the mild or remission grades, the tissue can also be characterized as fibrotic. If the change in the perfusion index is not large, but if a large increase in tissue thickness has occurred, then scarring is likely due to an increase in blood flow. Visually, the clinician may see a discoloration of the tissue (e.g., the tissue is less red) due to the lack of blood flow. If the thickness score and the perfusion index are within the same severity level, then the sample tissue can be characterized as having inflammation but not fibrosis because the increasing trajectories of the perfusion index and thickness are similar, indicating that increased blood flow leads to inflammation.

[0054] In step 214, the results may be displayed on a graphical user interface, such as user interface 16. The results displayed in step 214 may include the results of any of the above steps or any other results. The results displayed in step 214 may be in the form of a table, a graph, a chart, text, an animation, or any other form. Figure 4A-4B An exemplary graphical user interface display is shown.

[0055] Figure 4A-4B An exemplary graphical user interface (GUI) according to the present disclosure is shown. Figure 4A An exemplary GUI 300 is shown. Figure 2 As described above, GUI 300 may be the output from step 118. In an alternative embodiment, as described with respect to Figure 3 The GUI 300 may be an output from step 214, or an output from any other method. The GUI 300 may depict information such as thickness (and / or thickness score) 302, inflammation score 304, and fibrosis attribute 306. Thickness (and / or thickness score) 302 may be expressed in units of length (e.g., millimeters) and may reflect the value determined in steps 110 and / or 208. Inflammation score 304 may be expressed in percentages and may reflect the value determined in steps 112 and / or 210. Fibrosis attribute 306 may be expressed in terms of whether fibrosis is present, and may reflect the results of steps 116 and / or 212. For example, fibrosis attribute 306 may be "yes", "no", or may indicate uncertainty or a tendency toward fibrosis or no fibrosis.

[0056] Figure 4B An exemplary GUI 350 is depicted. Figure 3 As described above, GUI 350 may be the output from step 214. In an alternative, GUI 350 may be the output from step 118, as described above with respect to Figure 2 The GUI 350 may display information about a sample identification code 352, a perfusion index 354, a thickness (and / or thickness score) 356, and / or an inflammation score 358. The perfusion index 354 may represent a value determined in steps 104, 110, 204, and / or 208. The thickness 356 may represent a value determined in steps 104, 110, 204, and / or 208. The inflammation score 358 may represent a value determined in steps 106, 112, and / or 210. The perfusion index 354 may be expressed as a percentage. The thickness 356 may be expressed in units of length (e.g., millimeters). The inflammation score 358 may be expressed as a percentage, or may be unitless.

[0057] The GUI 350 may display information of a reference tissue 362 and sample tissues 364, 366. The GUI 350 may also depict information of a selected sample, which may be, for example, a currently tested sample. For example, the GUI 350 may depict a sample's thickness 372, inflammation score 374, and / or fibrosis attribute 376. Figure 4B As shown, thickness 372 and inflammation score 374 can correspond to sample 366. GUI 350 can also depict other properties of the sampled area. For example, GUI 350 can display a graph 378 that can depict the activity of the area, such as slow wave activity. Such a waveform can be a plethysmograph, which can be used to depict perfusion measurements from an optical sensor. The plethysmograph can be a slow wave of a blood flow waveform. GUI 300, GUI 350 can also depict other information, further details of the above information, or less information than the above information.

[0058] Although the principles of the present disclosure are described herein with reference to illustrative examples for specific applications, it should be understood that the present disclosure is not limited thereto. Those of ordinary skill in the art and those who obtain the teachings provided herein will recognize that other equivalent forms of modifications, applications and substitutions all fall within the scope of the examples described herein. Therefore, the present invention should not be considered to be limited by the foregoing description.

Claims

1. A method for evaluating the gastrointestinal tract, the method comprising: receiving, by a processor, data regarding tissue quality of the gastrointestinal tract obtained from a sensor located in a lumen of the gastrointestinal tract; using the data, determining, by the processor, a perfusion measurement of blood in the tissue; using the data, determining, by the processor, a thickness measurement of the tissue; determining, by the processor, an inflammation score based on the perfusion measurement and the thickness measurement of the tissue; as well as The inflammation score is compared, by the processor, to a reference inflammation score obtained from healthy tissue.

2. The method according to claim 1, in, The gastrointestinal tract tissue and the healthy tissue are from the same patient.

3. The method according to claim 1, in, The gastrointestinal tract tissue and the healthy tissue are from different patients.

4. The method according to claim 1, in, The reference inflammation score is derived based on healthy tissues of multiple patients.

5. The method according to claim 1, in, The reference inflammation score obtained from the healthy tissue is from a patient database.

6. The method according to claim 1, further comprising: include: A state of the tissue is classified, by the processor, based on the comparison.

7. The method of claim 1, further comprising: include: The tissue is classified, by the processor, as one of healthy tissue, fibrotic tissue, or inflamed tissue based on the comparison.

8. The method according to claim 1, in, Determining the inflammation score includes: applying a first function to the perfusion measurements; applying a second function to the thickness measurements; and The inflammation score is determined based on a first result of applying a first function to the perfusion measurement and a second result of applying a second function to the thickness measurement.

9. The method according to claim 1, in, One or more of the first function or the second function is a sigmoid function.

10. The method according to claim 1, in, The perfusion measurement is a perfusion index that includes the ratio of the amount of pulsatile light obtained by the sensor to the total amount of light obtained by the sensor.

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