Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm.

COVID-19-related pneumonia is typically diagnosed using chest x-ray or computed tomography images. However, these techniques can only be used in hospitals. In contrast, thermal cameras are portable, inexpensive devices that can be connected to smartphones. Thus, they can be used to detect and monitor medical conditions outside hospitals. Herein, a smartphone-based application using thermal images of a human back was developed for COVID-19 detection. Image analysis using a deep learning algorithm revealed a sensitivity and specificity of 88.7% and 92.3%, respectively. The findings support the future use of noninvasive thermal imaging in primary screening for COVID-19 and associated pneumonia.

[MOCK MOLE: PRODUCING SYNTHETIC IMAGES THAT RECAPITULATE CONFOCAL PATTERNS OF MELANOCYTIC NEVI VIA DEEP-LEARNING MODELS].

INTRODUCTION: Melanocytic nevi present microscopic patterns, which differ in their associated melanoma risk, and can be non-invasively recognized under Reflectance Confocal Microscopy (RCM). AIMS: To train a Generative Adversarial Network (GAN) deep-learning model to produce synthetic images that recapitulate RCM patterns of nevi, enabling reliable classification by human readers and by a Convolutional Neural Network (CNN) computer model. METHODS: A dataset of RCM images of nevi, presenting a uniform pattern, were chosen and classified into one of three patterns - Meshwork, Ring or Clod. Images were used for training a GAN model, which in turn, produced synthetic images recapitulating RCM patterns of nevi. A random sample of synthetic images was classified by two independent human readers and by a CNN model. Human and computer-model classifications were compared. RESULTS: The training set for the GAN model included 1496 RCM images, including 977 images (65.3%) with Meshwork pattern, 261 (17.4%) with Ring and 258 (17.2%) with Clod pattern. The GAN model produced 6000 synthetic RCM-like images. Of these, 302 images were randomly chosen and classified by human readers, including 83 (27.5%) classified as Meshwork, 131 (43.4%) as Ring, and 88 (29.1%) as Clod pattern. Human inter-observer concordance in pattern classification was 91.7%, and human-to-CNN concordance was 87.7%. CONCLUSIONS: We demonstrate feasibility of producing synthetic images, which recapitulate RCM patterns of nevi and can be reproducibly recognized by human readers and by deep-learning models. Synthetic image datasets may allow teaching RCM patterns to novices, training of computer models, and data sharing between research centers.

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.