Systems and methods for detection of infectious respiratory diseases

A system for detection of an infectious respiratory disease by implementation of an automated X-rays-based triage approach alongside algorithmic clinical sample pooling for molecular diagnosis, comprises

• a first subsystem for automating detection and localization of medical abnormalities on chest X-ray imaging scans using a deep learning algorithm carried out by a computer, wherein the deep learning algorithm is developed by the steps of:
• receiving and processing images from the chest X-ray scan imaging procedure data;
• detecting and localizing medical abnormalities of the images using a deep learning system, wherein the deep learning system carried out by a computer is developed by the steps of:
• generating a first score that corresponds to a level of recognition of each of the medical abnormalities and outputting a bounding box representing the precise location and extent of the medical abnormalities;
• and
• generating a second score for each subject that corresponds to a level of recognition of an infectious respiratory disease based on the first scores of the medical abnormalities that suggest typical or atypical the infectious respiratory disease;
• and
• a second subsystem for algorithmic pooling of clinical samples from each subject who also receives chest X-ray scans, wherein the algorithmic pooling is carried out by a computer following the steps of:
• mapping the second score derived from the first subsystem to a probability of a subject having the infectious respiratory diseases;
• ranking the clinical samples from lowest to highest based on the second score;
• and
• selecting a pooling size of the clinical samples for a molecular test based on a collective probability of all the subjects having the infectious respiratory disease in a pool, wherein the collective probability is a sum of the probability of each subject having the infectious respiratory disease in the pool and wherein the probability is lesser than or equal to 1.