Eko

DEEP LEARNING FOR AUTOMATED AORTIC STENOSIS AND VALVULAR HEART DISEASE DETECTION USING A DIGITAL STETHOSCOPE

DEEP LEARNING FOR ALGORITHMIC DETECTION OF PULMONARY HYPERTENSION USING A COMBINED DIGITAL STETHOSCOPE AND SINGLE-LEAD ELECTROCARDIOGRAM - ABSTRACT THIS SBIR DIRECT TO PHASE II PROJECT WILL DEVELOP A DEEP LEARNING-BASED CLINICAL DECISION SUPPORT ALGORITHM FOR SCREENING AND DETECTING PULMONARY HYPERTENSION (PH) USING PHONOCARDIOGRAM AND ELECTROCARDIOGRAM DATA RECORDING USING THE EKO DUO DIGITAL STETHOSCOPES. THE SCREENING TOOL WILL HELP TO DECREASE THE NUMBER OF PATIENTS WITH PULMONARY HYPERTENSION THAT REMAIN UNDERTREATED SIMPLY BECAUSE THEIR CONDITION IS NOT DIAGNOSED. THE GOLD STANDARD DIAGNOSTIC FOR PULMONARY HYPERTENSION IS RIGHT HEART CATHETERIZATION WHICH IS COSTLY, INVASIVE, AND REQUIRES SPECIALIZED PERSONNEL.TO ADDRESS THESE CHALLENGES, EKO DEVELOPED THE DUO, A DIGITAL STETHOSCOPE IN A HANDHELD FORM FACTOR WITH BUILT-IN SINGLE LEAD ELECTROCARDIOGRAM. THE DUO IS DESIGNED TO STREAM DIGITIZED PHONOCARDIOGRAMS AND ELECTROCARDIOGRAMS TO A SMARTPHONE, TABLET OR PERSONAL COMPUTER. THERE, THE SIGNAL CAN BE ANALYZED WITH THE DECISION SUPPORT ALGORITHM WE DEVELOPED AS PART OF THIS PROJECT. THE SPECIFIC AIMS OF THIS STUDY ARE : (1) BUILD A DATABASE OF MATCHED ECG/PCG RECORDING LABELED AGAINST RIGHT HEART CATHETERIZATION AND ECHOCARDIOGRAMS, AND (2) DEVELOP AND CLINICALLY TEST A DEEP LEARNING ALGORITHM THAT CAN DETECT PH AND STRATIFY ITS SEVERITY. BY INTEGRATING THIS DEEP LEARNING ALGORITHM INTO EKO’S MOBILE AND CLOUD SOFTWARE PLATFORM, WE ANTICIPATE THIS ALGORITHM WILL ENABLE MORE ACCURATE SCREENING FOR PULMONARY HYPERTENSION IN ADULT PATIENTS, LEADING TO EARLIER DIAGNOSIS AND BETTER PATIENT OUTCOMES.

DEEP LEARNING FOR AUTOMATED AORTIC STENOSIS AND VALVULAR HEART DISEASE DETECTION USING A DIGITAL STETHOSCOPE

DIALYSIS ACCESS MONITORING USING A DIGITAL STETHOSCOPE-BASED DEEP LEARNING SYSTEM - THIS SBIR PHASE I PROJECT WILL DEVELOP A DEEP LEARNING-BASED ALGORITHM TO ANALYZE THE SOUND OF BLOOD FLOW IN NEWLY CREATED ARTERIOVENOUS FISTULAS (AVFS) USED FOR HEMODIALYSIS ACCESS. THIS MONITORING TOOL CAN HELP TO IDENTIFY FISTULAS THAT ARE UNLIKELY TO MATURE IN PATIENTS WHO NEED SURGICAL INTERVENTION TO ACHIEVE SUCCESSFUL MATURATION. THE SPECIFIC AIMS OF THE STUDY ARE (1) TO CREATE THE WORLD’S FIRST DEEP LEARNING-SCALE DATABASE OF NEWLY CREATED AVF SOUNDS FROM HEMODIALYSIS PATIENTS, AND (2) DEVELOP AND EVALUATE THE PERFORMANCE OF A DEEP LEARNING CLASSIFICATION MODEL TRAINED VIA SEMI-SUPERVISED LEARNING TO DISCRIMINATE BETWEEN PATIENTS WITH AVFS LIKELY TO MATURE AND PATIENTS WITH AVFS UNLIKELY TO MATURE. BY INTEGRATING THIS DEEP LEARNING ALGORITHM INTO EKO’S MOBILE AND CLOUD SOFTWARE PLATFORM, WE ANTICIPATE THIS ALGORITHM WILL ENABLE BETTER MONITORING OF THE MATURATION PROCESS FOR NEWLY CREATED FISTULAS. DURING PHASE I OF THE PROJECT, WE WILL RECRUIT STUDY SUBJECTS IN ACCESS CENTERS AT THE UNIVERSITY OF NORTH CAROLINA (UNC).