Incorporating artificial intelligence in portable infrared thermal imaging for the diagnosis and staging of nonalcoholic fatty liver disease.

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is one of the most prevalent chronic liver diseases worldwide. Thermal imaging combined with advanced image-processing and machine learning analysis accurately classified disease status in a study on mice; this study aimed to develop this tool for humans. This prospective study included 46 patients who underwent liver biopsy. Liver thermal imaging was performed on the same day as liver biopsy. We developed an image-processing algorithm that measured the relative spatial thermal variation across the skin covering the liver. The texture parameters obtained from the thermal images were input into the machine learning algorithm. Patients were diagnosed with MASLD and stratified according to nonalcoholic fatty liver disease activity score (NAS) and fibrosis stage using the METAVIR score. Twenty-one of 46 patients were diagnosed with MASLD. Using thermal imaging followed by processing, detection accuracy for patients with NAS >4 was 0.72.

Natural history of changes in knee skin temperature following total knee arthroplasty: a systematic review and meta-analysis.

Patients undergoing total-knee arthroplasty (TKA) have transient increases in anterior knee skin temperature (ST) that subside as recovery progresses-except in cases of systemic or local prosthetic joint infections (PJI). This meta-analysis was designed to quantify the changes in knee ST following TKA in patients with uncomplicated recovery as a prerequisite for assessing the usefulness of thermal imaging for diagnosis of PJI. This meta-analysis (PROSPERO-CRD42021269864) was performed according to PRISMA guidelines. PUBMED and EMBASE were searched for studies reporting knee ST of patients that underwent unilateral TKA with uncomplicated recovery. The primary outcome was the weighted means of the differences in ST between the operated and the non-operated knees (ΔST) for each time point (before TKA, and 1 day; 1,2, and 6 weeks; and 3,6, and 12-months post-TKA). For this analysis, 318 patients were included from 10 studies. The elevation in ST was greatest during the first 2-weeks (ΔST = 2.8 °C) and remained higher than pre-surgery levels at 4-6 weeks. At 3-months, ΔST was 1.4 °C. It decreased to 0.9 °C and 0.6 °C at 6 and 12-months respectively. Establishing the baseline profile of knee ST following TKA provides the necessary first step for evaluating the usefulness of thermography for the diagnosis of post-procedural PJI.

Automatized Detection of Crohn's Disease in Intestinal Ultrasound Using Convolutional Neural Network.

INTRODUCTION: The use of intestinal ultrasound (IUS) for the diagnosis and follow-up of inflammatory bowel disease is steadily growing. Although access to educational platforms of IUS is feasible, novice ultrasound operators lack experience in performing and interpreting IUS. An artificial intelligence (AI)-based operator supporting system that automatically detects bowel wall inflammation may simplify the use of IUS by less experienced operators. Our aim was to develop and validate an artificial intelligence module that can distinguish bowel wall thickening (a surrogate of bowel inflammation) from normal bowel images of IUS. METHODS: We used a self-collected image data set to develop and validate a convolutional neural network module that can distinguish bowel wall thickening >3 mm (a surrogate of bowel inflammation) from normal bowel images of IUS. RESULTS: The data set consisted of 1008 images, distributed uniformly (50% normal images, 50% abnormal images). Execution of the training phase and the classification phase was performed using 805 and 203 images, respectively. The overall accuracy, sensitivity, and specificity for detection of bowel wall thickening were 90.1%, 86.4%, and 94%, respectively. The network exhibited an average area under the ROC curve of 0.9777 for this task. CONCLUSIONS: We developed a machine-learning module based on a pretrained convolutional neural network that is highly accurate in the recognition of bowel wall thickening on intestinal ultrasound images in Crohn's disease. Incorporation of convolutional neural network to IUS may facilitate the use of IUS by inexperienced operators and allow automatized detection of bowel inflammation and standardization of IUS imaging interpretation.

We developed a machine-learning module based on a pretrained convolutional neural network that is highly accurate in the recognition of bowel wall thickening on intestinal ultrasound images in Crohn's disease.

Automated thermal imaging monitors the local response to cervical cancer brachytherapy.

Malignant tumors have high metabolic and perfusion rates, which result in a unique temperature distribution as compared to healthy tissues. Here, we sought to characterize the thermal response of the cervix following brachytherapy in women with advanced cervical carcinoma. Six patients underwent imaging with a thermal camera before a brachytherapy treatment session and after a 7-day follow-up period. A designated algorithm was used to calculate and store the texture parameters of the examined tissues across all time points. We used supervised machine learning classification methods (K Nearest Neighbors and Support Vector Machine) and unsupervised machine learning classification (K-means). Our algorithms demonstrated a 100% detection rate for physiological changes in cervical tumors before and after brachytherapy. Thus, we showed that thermal imaging combined with advanced feature extraction could potentially be used to detect tissue-specific changes in the cervix in response to local brachytherapy for cervical cancer.

Assessment of blood distribution in response to post-surgical steal syndrome: A novel technique based on Thermo-Anatomical Segmentation.

The distal ischemic steal syndrome (ISS) is a complication following the construction of an arteriovenous (A-V) access for hemodialysis. The ability to non-invasively monitor changes in skin microcirculation improves both the diagnosis and treatment of vascular diseases. In this study, we propose a novel technique for evaluating the palms' blood distribution following arteriovenous access, based on thermal imaging. Furthermore, we utilize the thermal images to identify typical recovery patterns of patients that underwent this surgery and show that thermal images taken post-surgery reflect the patient's follow-up status. Thermal photographs were taken by a portable thermal camera from both hands before and after the A-V access surgery, and one month following the surgery, from ten dialysis patients. A novel term "Thermo-Anatomical Segmentation", which enables a functional assessment of palm blood distribution was defined. Based on this segmentation it was shown that the greatest change after surgery was in the most distal region, the fingertips (p < 0.05). In addition, the changes in palm blood distribution in both hands were synchronized, which indicates a bilateral effect. An unsupervised machine learning model revealed two variables that determine the recovery pattern following the surgery: the palms' temperature difference pre- and post-surgery and the post-surgery difference between the treated and untreated hand. Our proposed framework provides a new technique for quantitative assessment of the palm's blood distribution. This technique may improve the clinical treatment of patients with vascular disease, particularly the patient-specific follow-up, in clinics as well as in homecare.

Automated thermal imaging for the detection of fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) comprises a spectrum of progressive liver pathologies, ranging from simple steatosis to non-alcoholic steatohepatitis (NASH), fibrosis and cirrhosis. A liver biopsy is currently required to stratify high-risk patients, and predicting the degree of liver inflammation and fibrosis using non-invasive tests remains challenging. Here, we sought to develop a novel, cost-effective screening tool for NAFLD based on thermal imaging. We used a commercially available and non-invasive thermal camera and developed a new image processing algorithm to automatically predict disease status in a small animal model of fatty liver disease. To induce liver steatosis and inflammation, we fed C57/black female mice (8 weeks old) a methionine-choline deficient diet (MCD diet) for 6 weeks. We evaluated structural and functional liver changes by serial ultrasound studies, histopathological analysis, blood tests for liver enzymes and lipids, and measured liver inflammatory cell infiltration by flow cytometry. We developed an image processing algorithm that measures relative spatial thermal variation across the skin covering the liver. Thermal parameters including temperature variance, homogeneity levels and other textural features were fed as input to a t-SNE dimensionality reduction algorithm followed by k-means clustering. During weeks 3,4, and 5 of the experiment, our algorithm demonstrated a 100% detection rate and classified all mice correctly according to their disease status. Direct thermal imaging of the liver confirmed the presence of changes in surface thermography in diseased livers. We conclude that non-invasive thermal imaging combined with advanced image processing and machine learning-based analysis successfully correlates surface thermography with liver steatosis and inflammation in mice. Future development of this screening tool may improve our ability to study, diagnose and treat liver disease.

Microcirculatory Response to Photobiomodulation-Why Some Respond and Others Do Not: A Randomized Controlled Study.

BACKGROUND AND OBJECTIVES: Photobiomodulation (PBM), a non-ionizing, non-thermal irradiation, used clinically to accelerate wound healing and inhibit pain, was previously shown to increase blood flow. However, some individuals respond to PBM, but others do not. The purpose of this study was to investigate factors affecting this patient-specific response using advanced, noninvasive methods for monitoring microcirculatory activity. STUDY DESIGN/MATERIALS AND METHODS: In this prospective, randomized controlled clinical trial (NCT03357523), 20 healthy non-smoking volunteers (10:10 males:females, 30 ± 8 years old) were randomized to receive either red- (633 nm and 70 W/cm2 ) or near-infrared light (830 nm and 55 mW/cm2 ) over the wrist for 5 minutes. Photoplethysmography, laser Doppler flowmetry, and thermal imaging were used to monitor palm microcirculatory blood volume, blood flow, and skin temperature, respectively, before, during, and 20 minutes after irradiation. Participants with skin temperature change ≥0.5°C from baseline were considered "responders". RESULTS: Near-infrared PBM was found to induce a 27% increase in microcirculatory flow that increased to 54% during the 20-minute follow-up period (P = 0.049 and P = 0.004, respectively), but red light PBM did not increase the median flow. Only 10 of 20 participants were responders by thermal imaging (i.e., ≥0.5°C from baseline), and their initial skin temperature was between 33 and 37.5°C. The non-responders had either "hot" hands (≥37.5°C) or "cold" hands (≤33°C). In responders, the meantime to 20% increase in microcirculatory blood volume and blood flow was less than 2.5 minutes after initiation of PBM irradiation. CONCLUSIONS: We demonstrated that PBM induces arteriolar vasodilatation that results in both immediate and long-lasting increased capillary flow and tissue perfusion in healthy individuals. This response was wavelength-dependent and modified by skin temperature. These findings regarding physiological parameters associated with sensitivity or resistance to PBM provide information of direct relevance for patient-specific therapy. Lasers Surg. Med. © 2020 Wiley Periodicals, Inc.

Thermal Monitoring of Tumor and Tissue State during Radiation Therapy - A Complex Case of Radiation Recall.

Common radiation dermatitis over radiation fields can be mild as minor erythema but can also be associated with blisters and skin desquamation. This phenomenon has been widely investigated and documented, especially in breast cancer patients. Obesity, smoking, and diabetes are known risk factors; however, we cannot predict the severity of radiation dermatitis prior to treatment. The overwhelming radiation recall dermatitis is an acute inflammatory reaction confined to previously irradiated areas that can be triggered when chemotherapy agents are administered after radiotherapy. This rare, painful skin reaction leads to treatment cessation or alteration. In this study, we investigate the feasibility of using thermography as a tool to predict the response of normal breast tissue and skin to radiation therapy and the risk of developing radiation recall dermatitis. Six women with viable in-breast tumor (breast cancer) and eight women who underwent tumor resection (lumpectomy) were monitored by a thermal camera prior to radiotherapy treatment (breast region) and on weekly basis, in the same environmental conditions, through the radiation course of treatment. One patient developed radiation recall dermatitis when treated with chemotherapy following radiation therapy, and needed intensive local treatments and narcotics with full recovery thereafter. Clinical and treatment data as well as response to radiation were collected prospectively. The ongoing thermal changes observed during the radiation treatment for all patients, with and without viable tumor in the breast, were documented, analyzed, and reported here with detailed comparison to the recognized data for the patient diagnosed with radiation recall dermatitis.

Thermal imaging as a tool for evaluating tumor treatment efficacy.

Breast cancer is the most frequently diagnosed cancer among women in the Western world. Thermography is a nonionizing, noninvasive, portable, and low-cost method that can be used in an outpatient clinic. It was tried as a tool to detect breast cancer tumors, however, it had too many false readings. Thermography has been extensively studied as a breast cancer detection tool but was not used as a treatment monitoring tool. The purpose of this study was to investigate the possibility of using thermal imaging as a feedback system to optimize radiation therapy. Patients were imaged with a thermal camera prior and throughout the radiotherapy sessions. At the end of the session, the images were analyzed for temporal vasculature changes through vessels segmentation image processing tools. Tumors that were not responsive to treatment were observed before the radiation therapy sessions were concluded. Assessing the efficacy of radiotherapy during treatment makes it possible to change the treatment regimen, dose, and radiation field during treatment as well as to individualize treatment schedules to optimize treatment effectiveness.

Thermographic investigation of tumor size, and its correlation to tumor relative temperature, in mice with transplantable solid breast carcinoma.

Treating cancer is one of the major challenges of modern medicine. Since mice models are an important tool in cancer treatment research, it is required to assess murine tumor development. Existing methods for investigating tumor development are either high cost and limited by their availability or suffer from low accuracy and reproducibility. In order to overcome these drawbacks, thermography may be used. DA3 breast cancer carcinoma tumors in 12 Balb/c mice were thermally imaged and monitored for a period of several weeks. Eight mice were treated with diffusing alpha emitters radiation therapy (DaRT) wires, while four were treated with inert wires. For large tumors, the area was estimated by analyzing thermal images and was found to be in correlation with manual caliper measurements. In addition, the correlation between tumor area and relative temperatures was calculated and compared to previous works. Temperature differences were larger for tumors treated with DaRT wires than tumors with inert wires. These correlations can be used to assist in tumor size estimation and reveal information regarding its metabolic state. Overall, thermography was shown to be a promising tool for assessing tumor development with the additional advantages of being nonradiative and potentially providing indication of intratumoral biological processes.