Observational Study to Evaluate the Clinical Efficacy of Thermalytix for Detecting Breast Cancer in Symptomatic and Asymptomatic Women.

Purpose: To evaluate the sensitivity and specificity of Thermalytix, an artificial intelligence-based computer-aided diagnostics (CADx) engine, to detect breast malignancy by comparing the CADx output with the final diagnosis derived using standard screening modalities.

Methods: This multisite observational study included 470 symptomatic and asymptomatic women who presented for a breast health checkup in two centers. Among them, 238 women had symptoms such as breast lump, nipple discharge, or breast pain, and the rest were asymptomatic.

Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics.

Motivation: Breast cancer is the leading cause of cancer deaths among women today. Survival rates in developing countries are around 50%-60% due to late detection. A personalized, accurate risk scoring method can help in targeting the right population for follow-up tests and enables early detection of breast abnormalities.

Cascaded CNN for View Independent Breast Segmentation in Thermal Images.

Breast Cancer is the leading cause of cancer deaths in women today. Use of thermal imaging for early stage breast cancer screening is gaining more adoption in recent times and automated analysis of these thermal images with computer aided diagnosis is the key to maintain objectivity in assessment and improve quality of diagnosis. One of the main challenges in automated breast thermography is accurate segmentation of breast region robust to technician errors in image capture - such as view, distance from imaging device, position, etc.

A study of the effect of subject motion to pulse rate estimation.

We presented a systematic study of how subject head motion affects pulse rate estimation using photoplethysmography from the subject's face. We evaluated the performance at various steps in the process, including object tracking, skin blob detection, pulse signal extraction and pulse rate estimation. We demonstrated that the signal-to-noise ratio of the power spectrum is a good indicator of signal artifacts induced by subject motion, thus can be used as a quantitative metric in continuous pulse rate monitoring to reduce estimation errors.

A view invariant gait cycle segmentation for ambient monitoring.

Gait analysis has many clinical applications in disease detection and treatment evaluation. Gait cycle segmentation is a critical component in gait analysis for timing the gait phases in evaluating many movement disorders. Computer vision techniques have been widely used in surveillance for security monitoring.

Extraction of medically interpretable features for classification of malignancy in breast thermography.

Thermography, with high-resolution cameras, is being re-investigated as a possible breast cancer screening imaging modality, as it does not have the harmful radiation effects of mammography. This paper focuses on automatic extraction of medically interpretable non-vascular thermal features. We design these features to differentiate malignancy from different non-malignancy conditions, including hormone sensitive tissues and certain benign conditions, which have an increased thermal response.