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

Abstract

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.

Description

[0001] Cross-references to related literature

[0002] 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

[0003] 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

[0004] Inflammatory bowel disease ("IBD") is a chronic condition characterized by chronic inflammation of the gastrointestinal ("GI") tract. It affects 5-6 million people worldwide and approximately 1.6 million patients in the United States. Treating IBD can cost patients and healthcare providers a significant amount of money each year, with direct costs estimated to be between $11 billion and $28 billion annually in the United States. Furthermore, patients with IBD may experience longer hospital stays, higher costs, and higher readmission rates than those without IBD.

[0005] 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 transmural involvement. Symptoms of CD include abdominal pain, fever, cramping, rectal bleeding, and frequent diarrhea. The peak age of onset of CD is between 15 and 35 years old. UC affects only the colon, affecting the mucosa. UC has mild to severe symptoms that are similar to those of CD. UC may have fewer complications than CD. Colectomy can be used to treat UC. The peak age of onset of UC is between 15 and 30 years old and 50 and 70 years old. 55% of people with IBD have UC, and 45% of people with IBD have CD.

[0006] 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 treatments lack the ability to robustly assess GI tract inflammation and the presence of fibrosis. Techniques 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 GI tract fibrosis, physicians' ability to effectively treat the disease may be limited. Summary of the Invention

[0007] Examples of the present disclosure relate to devices, systems, and / or methods for diagnosing and / or monitoring disease, such as by determining inflammation and / or fibrosis. Each example disclosed herein may include one or more features related to any other disclosed example.

[0008] 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 a state of the tissue.

[0009] Any of the methods described herein may include one or more of the following features or steps. Classifying the state of a tissue may include classifying a fibrosis property of the tissue. Classifying the fibrosis 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 pulsatile light to 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 a result of the first function and a 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 a difference between the perfusion measurement and a reference perfusion measurement. 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 can include classifying the perfusion measurement and classifying the inflammation measurement. The method can further include comparing the inflammation measurement to a reference measurement of inflammation. The reference measurement can represent the amount of inflammation in healthy tissue.

[0010] A method for evaluating the gastrointestinal tract of a subject may include using a sensor positioned in the subject to obtain data regarding tissue quality of the gastrointestinal tract; using the obtained data to determine a blood perfusion measurement in the tissue; using the obtained data to determine a thickness measurement of the tissue; using the perfusion measurement and the thickness measurement to determine an inflammation measurement of the tissue; and using one or more of the perfusion measurement, the thickness measurement, and the inflammation measurement to classify a state of the tissue.

[0011] Any of the methods described herein may include one or more of the following features or steps. Classifying the state of the tissue may include classifying a fibrotic property of the tissue. The sensor may be an optical sensor positioned in the lumen of the gastrointestinal tract. 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 a result of the first function and a 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 a 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 to a reference measurement of inflammation.

[0012] In another example, a method for assessing the gastrointestinal tract may include using a sensor positioned within the lumen of the gastrointestinal tract to obtain data regarding the mass 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 to characterize a fibrotic property of the tissue. Any of the methods 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 a result of the first function and a result of the second function. Applying the first function may include applying a weighting value. Applying the first function may include applying a sigmoid function.

[0013] 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.

[0014] It will be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the claimed invention. As used herein, the terms "comprises," "comprising," or any other variations thereof, are intended to encompass a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those 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

[0015] 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.

[0016] Figure 1 Exemplary systems for scoring inflammation and determining fibrosis are described.

[0017] Figure 2-3 Exemplary systems for scoring inflammation and determining fibrosis are described.

[0018] Figures 4A-4B An exemplary user interface for displaying information about a disease state is described. DETAILED DESCRIPTION

[0019] The present disclosure is directed to the equipment, system and / or method for diagnosing and / or monitoring disease by, for example, determining inflammation and / or fibrosis. Specifically, in at least some aspects, equipment, system and / or method can be used for using the index of perfusion and tissue thickening to score inflammation and determine fibrosis. As described herein, the equipment, system and method for diagnosing and / or monitoring disease by, for example, scoring inflammation and determining fibrosis can also be used for monitoring other diseases, including diseases such as 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 disclosed systems. 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-mentioned conditions or the impact caused by the above-mentioned conditions.

[0020] Figure 1An exemplary system 10 for scoring inflammation and determining fibrosis is described. System 10 may include one or more software applications 12. Software applications 12 may interact with, for example, a medical device 14, a user interface 16, and a memory 18. Software applications 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.

[0021] The medical device 14 may include an imager capable of capturing images, including optical, infrared, thermal or other images. The medical device 14 may also include other types of sensors or inputs, including optical sensors, ultrasonic sensors and / or photodiodes. The medical device 14 may be capable of capturing still images, video images, or both. The medical device 14 may be configured to transmit images or other data to a receiving device via a wired or wireless connection. The 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 implantable monitors or other devices. In the case of an implantable monitor, such an implantable monitor may be a permanent or temporary implant. Examples of medical devices suitable for use in connection with 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.

[0022] The medical device 14 can be configured to capture data at one or more locations in the GI tract, including the esophagus, stomach, duodenum, small intestine, and / or colon. For example, during an endoscopic procedure, the medical device 14 carried on an endoscope or a tool deployed within the endoscope (or simply the endoscope itself, if equipped with appropriate imaging components) can be advanced through various portions of the GI tract and can capture images or other data at a given portion or at numerous locations across the 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, can be capable of determining the location in the GI tract where data was 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, can be capable of determining whether images or other data were 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 can capture data at various points as it traverses the GI 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, the 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.

[0023] Medical device 14 can be configured to capture data regarding blood perfusion into GI tract tissue. For example, medical device 14 can be configured to capture data regarding blood penetration into tissue. To capture perfusion data, medical device 14 can include a light source and a receiver. Medical device 14 can include, for example, an optical sensor. For example, medical device 14 can include an LED light source and a photodiode receiver. Alternatively, medical device 14 can include an alternative light source, such as a light guide, optical fiber, or other light source, such as a laser pulsed at a specific wavelength (e.g., 530 nm and / or 930 nm), for non-invasive transperitoneal photoacoustic stimulation. Medical device 14 can also include an alternative receiver, such as an acoustic transducer receiver. Medical device 14 can capture information regarding the absorption and reflection of light waves in a region of interest in the GI tract. The captured information can include, for example, data indicating the amount of pulsed light (e.g., varying light) received by the sensor of medical device 14. The sensor of medical device 14 can capture information regarding the AC component of the captured light data, which can indicate the amount of pulsed light measured. The captured information can 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.

[0024] The medical device 14 can also capture data related to tissue thickness. Tissue thickening can be a result of increased blood flow to the tissue and blood vessels and / or inflammation can become occluded. 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 distinct emitter, the different absorbance in the tissue from the excitation point can have a linear relationship with the tissue thickness. Given a set fixed distance between the light source and the receiver, different depths of the tissue can provide different slopes of the linear relationship.

[0025] An implantable device, including one or more medical devices 14, can be delivered during a colonoscopy through a natural orifice endoscopic surgery (NOTES) procedure. For example, during a colonoscopy, an incision can be made and the sensor can be implanted outside the lumen on the omentum. Using such a procedure can have advantages, including using an endoscopy suite instead of an operating room. If the medical device 14 requires batteries, the batteries can be replaced during a routine colonoscopy. The medical device 14 can also be delivered through a laparoscopic procedure or a different surgical or non-surgical procedure.

[0026] 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 embodiment, 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 endoscope system, an endoscope tool, a wireless endoscope 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.

[0027] 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.

[0028] The software application 12 may store information related to algorithms, imaging data, or other data in memory 18. Data input from, for example, the medical device 14 may be stored by the software application 12 locally in memory 18 on a dedicated device or a general-purpose device such as a smartphone or computer. Memory 18 may be used for short-term storage of information. For example, memory 18 may be RAM memory. Memory 18 may also or alternatively be used for long-term storage of information. For example, memory 18 may be flash memory or solid-state memory. Alternatively, data from the medical device 14 may be stored remotely in memory 18 by the software application 12, such as in a cloud-based computing system.

[0029] The user interface 16 can allow users, such as medical professionals, patients, or other users, to access information stored in the memory 18 or other information generated by the software application 12. The user interface 16 can allow users to view and input data through a terminal, handheld device, mobile device, desktop device, or any other suitable device. The user interface 16 can facilitate interaction by multiple users simultaneously or sequentially. The user interface 16 can provide text, graphics, video, audio, or other output.

[0030] 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 variety of analyses and generate various graphs or other data.

[0031] 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 method described above with respect to Figure 1 The described system, such as system 10, can acquire reference data from healthy tissue of the GI tract. For example, the medical device 14 of system 10 can be used to acquire such data, which can, for example, indicate the amount of light absorbed and / or reflected by healthy tissue and can be collected from, for example, a photodiode and / or an optical array. The data acquired in step 102 can be from a subject whose fibrotic properties of the GI tract need to be characterized. In an alternative embodiment, the data acquired in step 102 can be from a different subject, such as a healthy subject. In another alternative embodiment, the data acquired in step 102 can be based on data from multiple subjects. For example, the data acquired in step 102 can be acquired from a patient database.

[0032] 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 the perfusion of blood into the tissue of the GI tract. 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 pulsed 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:

[0033]

[0034] A perfusion index of less than 2% may indicate hypoperfusion. 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 exceeding 8% may indicate hyperperfusion. The above formula is merely exemplary. Any other suitable method may be used to calculate 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.

[0035] The software application 12 can also use data from the medical device 14 to characterize the thickness of 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 unit of length (e.g., millimeters). For a healthy subject, the jejunum or ileum can have a thickness of approximately 1.1 mm-2 mm, the sigmoid colon can have a thickness of approximately 1.4 mm-3 mm, and the ascending colon can have a thickness of approximately 1.1 mm-2.5 mm. Increases in tissue thickness can be assessed in areas of interest that show visible signs of inflammation, such as redness, ulcers, "cobblestone-like" and / or strictures. Increases in tissue thickness can be measured compared to a baseline measurement previously made for the subject or from measurements made in a healthy area located near the affected area. Differences in tissue thickness can be assessed, with the affected tissue appearing to be 2-4 times thicker than the healthy area or baseline area. The thickness score or changes in thickness score can tend to follow a sigmoid function.

[0036] 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 sum 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 the sigmoid function and e represents the mathematical constant "e."

[0037]

[0038] 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 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 function applied to the logarithm of the perfusion index may be weighted less than the sigmoid function applied to the logarithm of the thickness. For example, a weighting factor of 1 / 3 may be applied to the sigmoid function applied to the logarithm of the perfusion index and a weighting factor of 2 / 3 may be applied to the sigmoid function applied to the logarithm of the thickness. For example, the inflammation score may be calculated according to the following formula:

[0039] I={C1S(log(P))+C2S(log(T))}×100%

[0040] Wherein I is the inflammation score (expressed as a percentage), C1 is the first constant, C2 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) can be divided by 100 before inserting the perfusion index into the above-mentioned inflammation score formula. C1 in any of the formulas herein can be equal to 1 / 3 or equal to 0.33. C2 in any of the formulas herein can be equal to 2 / 3 or equal to 0.67. These values ​​of constants C1 and C2 are merely exemplary. The constants can have any suitable values, which can be determined in any suitable manner. For example, the constants can be determined empirically based on a specific patient, a portion of patients, or the population as a whole. Expanding the above formula, the inflammation score can be calculated according to the following formula:

[0041]

[0042] Any of the above-described systems or methods can also be used to aggregate patient data. Such systems or methods can compare information collected from a specific patient with data collected from other patients and / or manually entered data regarding patient categorization. For example, any of the above-described systems or methods can utilize a library of conditions. Such systems and methods can be used to stratify patients based on their risk of exacerbating their disease state. This stratification can be based on previously collected data from a specific patient, a specific patient population, or an entire patient population.

[0043] In step 108, as described above with respect to Figure 1The system 10 described above 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 of the 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 requiring evaluation 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.

[0044] In step 110, a component of system 10, such as software application 12, may characterize the 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 alternative steps may be used.

[0045] In step 112, the software application 12 may characterize inflammation in the sample tissue using the data collected and / or characterized in steps 108 and / or 110. For example, the software application 12 may determine an inflammation score for the sample tissue. Step 112 may utilize any of the techniques discussed above with respect to step 106, or may utilize additional or alternative techniques.

[0046] In step 114, the software application 12 may compare the 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 the difference between the sample inflammation score and the reference inflammation score. Alternatively, 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. Alternatively, step 114 may be omitted.

[0047] 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 ).

[0048] 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 the reference inflammation score and the 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 categorized as "Inflammation" 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, a change 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.

[0049] As an example, the software application 12 can 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 can be used as a composite score, and further review of the base thickness score and perfusion index is required only if the inflammation score is greater than 50%.

[0050] If the inflammation score indicates unhealthy tissue, the software application 12 may further analyze the thickness score and / or perfusion index. If the perfusion index and thickness score increase at approximately the same rate, the tissue may be characterized as solely inflamed, resulting in edema. If the rate of increase in tissue thickness is greater than the rate of increase in perfusion index, the tissue may be classified as fibrotic, as more scarring has occurred, resulting in tissue thickening. When analyzing the tissue, the software application 12 may apply the aforementioned categories: "healthy," "fibrotic," and "inflammation." Alternatively, the software application 12 may apply the aforementioned categories of "mild," "moderate," and "severe," with the categories increasing from "mild" to "severe." Thickness changes that are higher than perfusion changes may indicate fibrotic tissue. On the other hand, thickness changes that are similar in magnitude to perfusion changes may indicate edema and / or inflammation. The above 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.

[0051] 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. Figures 4A-4B An exemplary graphical user interface display is shown.

[0052] 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.

[0053] In step 202, such as the above Figure 1 The system 10 can acquire 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 acquire reference data. In step 204, components 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 acquired 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 104 can be performed to acquire 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 utilize any of the techniques described with respect to steps 104, 110, and / or 204, or alternative steps can be utilized.

[0054] 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 the 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 the difference in perfusion index between the reference perfusion index and / or reference thickness score characterized in step 204 and the sample perfusion index and / or 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 as or different from 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:

[0055] I=C1ΔP+C2ΔT

[0056] Where I is the inflammation score (expressed as a percentage), C1 is the first constant, C2 is the second constant, P is the perfusion index, and T is the thickness score. C1 in any of the formulas herein can be equal to 1 / 3 or equal to 0.33. C2 in any of the formulas herein can be equal to 2 / 3 or equal to 0.67. These values ​​for constants C1 and C2 are merely exemplary. The constants can have any suitable values ​​and can be determined in any suitable manner. For example, the constants 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 remission. 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.

[0057] The change in perfusion index, ΔP, can be calculated by subtracting a first perfusion index (e.g., the reference perfusion index determined in step 204) from a second perfusion index (e.g., the sample perfusion index determined in step 208). The result can be divided by the first perfusion index and multiplied by 100 to provide the change in perfusion index in percent. For example, the change in perfusion index can be calculated using the following formula:

[0058]

[0059] where ΔP is the change in perfusion index, P n-1 is the first perfusion index, P n is a second perfusion index. A change in perfusion index of less than or equal to 25% (compared to a healthy reference sample) may indicate remission. A change in perfusion index of 25% to 90% may indicate mild to moderate disease. A change in perfusion index 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.

[0060] The change in thickness score, ΔT, can be calculated by subtracting a first thickness score (e.g., the reference thickness score determined in step 204) from a second thickness score (e.g., the 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:

[0061]

[0062] 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 thickness score can be followed by a sigmoid function. Applying the sigmoid function can constrain the score to be between 0 and 1 (multiplied by 100%). Applying the sigmoid function can similarly constrain the inflammation score comprising the thickness score or the change in thickness score. A change in thickness score of less than or equal to 25% (compared to a healthy reference sample) can indicate remission. A change in thickness score between 25% and 90% can indicate mild to moderate disease. A change in thickness score greater than 90% can indicate moderate to severe disease.

[0063] In step 212, the fibrosis attributes 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 regarding 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 attributes can be characterized using the values ​​determined above, including an inflammation score, a change in thickness score, and a change in perfusion index. As described above, the inflammation score can be used as a composite score. If the inflammation score falls within a certain range (e.g., above 50%, or above 25%), the change in thickness score and / or the change in perfusion are further analyzed. If the inflammation score is within a healthy range, further analysis may not be required.

[0064] In general, if the change in perfusion index is more severe than the change in thickness score, the tissue may be labeled as fibrotic. As mentioned 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.

[0065] 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 thickness score and the change in perfusion index). If the thickness score has increased, but the increase in perfusion is minimal, 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 mild or remission, the tissue can also be characterized as fibrotic. If the change in perfusion index is not significant, but if there is a significant increase in tissue thickness, scarring is likely due to an increase in blood flow. Visually, the clinician may see a discoloration in the tissue (e.g., the tissue is less red) due to the lack of blood flow. If the thickness score and perfusion index are within the same severity level, 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.

[0066] 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. Figures 4A-4B An exemplary graphical user interface display is shown.

[0067] Figures 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, GUI 300 may be the output from step 118. In an alternative embodiment, as described with respect to Figure 3 The GUI 300 can be an output from step 214, or an output from any other method. The GUI 300 can depict information such as thickness (and / or thickness score) 302, inflammation score 304, and fibrosis attribute 306. Thickness (and / or thickness score) 302 can be expressed in units of length (e.g., millimeters) and can reflect the value determined in steps 110 and / or 208. The inflammation score 304 can be expressed in percentages and can reflect the value determined in steps 112 and / or 210. The fibrosis attribute 306 can be expressed in terms of whether fibrosis is present and can reflect the results of steps 116 and / or 212. For example, the fibrosis attribute 306 can be "yes," "no," or can indicate uncertainty or a tendency toward fibrosis or the absence of fibrosis.

[0068] 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 the value determined in steps 104, 110, 204, and / or 208. The thickness 356 may represent the value determined in steps 104, 110, 204, and / or 208. The inflammation score 358 may represent the 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.

[0069] The GUI 350 may display information about a reference tissue 362 and sample tissues 364 and 366. The GUI 350 may also depict information about a selected sample, which may be, for example, the sample currently being examined. For example, the GUI 350 may depict the sample's thickness 372, inflammation score 374, and / or fibrosis attribute 376. Figure 4B As shown, thickness 372 and inflammation score 374 may correspond to sample 366. GUI 350 may also depict other properties of the sampled area. For example, GUI 350 may display a graph 378 that may depict activity in the area, such as slow wave activity. Such a waveform may be a plethysmograph, which may be used to depict perfusion measurements from an optical sensor. The plethysmograph may be a slow wave of a blood flow waveform. GUI 300, GUI 350 may also depict other information, further details of the above information, or less information than the above information.

[0070] 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 skilled in the art and having access to the teachings provided herein will recognize that other equivalent modifications, applications, and substitutions 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 of a subject by a medical device, the method comprising: placing a sensor in the lumen of the gastrointestinal tract using the medical device to obtain data regarding tissue quality of the gastrointestinal tract; a processor in communication with the medical device determining a blood perfusion measurement in the tissue using the acquired data; The processor, which is in communication with the medical device, uses the data to determine a thickness measurement of the tissue; the processor in communication with the medical device determining an inflammation measurement of the tissue using the perfusion measurement and the thickness measurement; as well as the processor in communication with the medical device classifying a state of the tissue using one or more of the perfusion measurement, the thickness measurement, and the inflammation measurement; wherein classifying the state of the tissue comprises classifying the fibrosis property of the tissue; wherein the perfusion measurement is a perfusion index, wherein the perfusion index is a ratio of pulsatile light amount to total light amount; applying a first function to the perfusion measurements; and applying a second function to the thickness measurements; wherein determining the inflammation measurement further comprises adding a result of the first function and a result of the second function; Wherein applying the first function comprises applying a sigmoid function; and Wherein applying the second function comprises applying a sigmoid function. 2 . The method of claim 1 , wherein classifying a fibrosis attribute comprises determining whether the tissue is fibrotic. The method of claim 1 , wherein applying the first function comprises applying a weighted value. 4 . The method of claim 1 , wherein applying the first function comprises calculating a difference between the perfusion measurement and a reference measurement of perfusion.

5. The method of claim 1, wherein using one or more of the perfusion measurement, the thickness measurement, and the inflammation measurement comprises using at least two of the perfusion measurement, the thickness measurement, and the inflammation measurement.

6. The method of claim 1, wherein using one or more of the perfusion measurement, the thickness measurement, and the inflammation measurement comprises using each of the perfusion measurement, the thickness measurement, and the inflammation measurement.

7. The method of claim 6, wherein using the perfusion measurement, the thickness measurement, and the inflammation measurement comprises classifying the perfusion measurement and classifying the inflammation measurement.

8. The method of claim 1, further comprising comparing the measure of inflammation to a reference measure of inflammation.

9. The method of claim 8, wherein the reference measurement represents the amount of inflammation in healthy tissue.

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