Challenges of AI driven diagnosis of chest X-rays transmitted through smart phones: a case study in COVID-19.

Healthcare delivery during the initial days of outbreak of COVID-19 pandemic was badly impacted due to large number of severely infected patients posing an unprecedented global challenge. Although the importance of Chest X-rays (CXRs) in meeting this challenge has now been widely recognized, speedy diagnosis of CXRs remains an outstanding challenge because of fewer Radiologists. The exponential increase in Smart Phone ownership globally, including LMICs, provides an opportunity for exploring AI-driven diagnostic tools when provided with large volumes of CXRs transmitted through Smart Phones.

A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital.

Objective: Artificial intelligence-enhanced breast thermography is being evaluated as an ancillary modality in the evaluation of breast disease. The objective of this study was to evaluate the clinical performance of Thermalytix, a CE-marked, AI-based thermal imaging test, with respect to conventional mammography.

Methods: A prospective, comparative study performed between 15 December 2018 and 06 January 2020 evaluated the performance of Thermalytix in 459 women with both dense and nondense breast tissue.

Evaluation of Non-Invasive Thermal Imaging for Detection of Viability of Onchocerciasis Worms.

Onchocerciasis is causing blindness in over half a million people in the world today. Drug development for the disease is crippled as there is no way of measuring effectiveness of the drug without an invasive procedure. Drug efficacy measurement through assessment of viability of onchocerca worms requires the patients to undergo nodulectomy which is invasive, expensive, time-consuming, skill-dependent, infrastructure dependent and lengthy process.

Automated vascular analysis of breast thermograms with interpretable features.

Vascular changes are observed from initial stages of breast cancer, and monitoring of vessel structures helps in early detection of malignancies. In recent years, thermal imaging is being evaluated as a low-cost imaging modality to visualize and analyze early vascularity. However, visual inspection of thermal vascularity is challenging and subjective.

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.

Robust Estimation of Breast Cancer Incidence Risk in Presence of Incomplete or Inaccurate Information.

Purpose: To evaluate the robustness of multiple machine learning classifiers for breast cancer risk estimation in the presence of incomplete or inaccurate information.

Data And Methods: Open data for this study was obtained from the BCSC Data Resource (http://breastscreening.cancer.

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.