Cognitive Medical and Industrial Inspection System and Method

The present invention relates to inspection of medical patients including, but not limited to, phonocardiography, auscultation and ultrasound medical imaging and other non-acoustical inspection techniques; and industrial non-destructive testing and evaluation of materials, structural components and machinery; and more particularly to the incorporation of cognitive artificial intelligence into an inspection system and method that utilizes cognitive mathematical techniques which emulate the cognitive processing abilities of the human brain including, but not limited to, symbolic cognitive architectures and inference process algebras, to analyze data collected from infrasound acoustical sensors (0.1 Hz-20 Hz), audible acoustical sensors (20 Hz to 20 kHz), ultrasound acoustical sensors and transmitters above 20 kHz, data collected from other non-acoustical inspection devices and systems including, but not limited to electrocardiography (EKG), computed-tomography (CT), single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic resonance imaging (MRI), electromagnetic testing (ET), magnetic particle inspection (MT or MPI), magnetic flux leakage testing (MFL), liquid penetrant, radiographic (x-ray and gamma ray), eddy-current testing, low coherence interferometry, and combinations thereof (i.e., multi-modality inspection data); fuse this data resulting in the generation of new metadata; and then utilize cognitive mathematical techniques to interpret this data against inspection signatures that characterize conditions being diagnosed. The present invention has the ability to also identify and anticipate abnormal conditions that fall outside known inspection signature patterns; and communicate the inspection results to an operator thereby simplifying the initial inspection and diagnosis for medical patients and industrial objects; minimizing false negative and false positive initial inspection results and lowering costs.