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Aswin Jose

Publications and source records attributed to Aswin Jose.

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Sonus Health: Calibrated Heart-Murmur Detection from Smartphone-Based Veterinary Auscultation

Heart disease is among the most common serious conditions in dogs and cats, and a heart murmur heard on auscultation is one of the earliest signs of such disease; such murmurs are often subtle and challenging to detect at early stages. General-practice veterinary examinations catch only a fraction of these murmurs, and definitive cardiac assessment typically requires either a board-certified cardiologist or an in-clinic echocardiogram, which may involve cost and scheduling constraints. We describe Sonus Health, a smartphone-based screening system that analyses an auscultation recording of approximately thirty seconds or longer - captured by a pet owner at home or by a veterinarian or nurse in clinic - and returns a tiered result within moments. The system was evaluated on 322 veterinary-labelled recordings under standard out-of-fold cross-validation. For recordings assigned to the high-confidence tier (30% of cases), accuracy reaches 95.9%, with 94.0% sensitivity and 97.9% specificity. Uncertain cases are prospectively routed to veterinary review rather than assigned an automated classification. Results are stable across standard and group-aware cross-validation, a held-out test split, and multiple random seeds. Beyond murmur detection, the platform also estimates heart rate and heart rate variability, and we position it as a screening, triage, and longitudinal-monitoring layer for companion-animal cardiac care.

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Instant Preliminary Cardiac Analysis from Smartphone Auscultation: A Real-World Canine Heart Sound Dataset and Evaluation

This study presents a real-world canine heart sound dataset and evaluates SoNUS version 3.2.x, a machine learning algorithm for preliminary cardiac analysis using smartphone microphone recordings. More than one hundred recordings were collected from dogs across four continents, with thirty eight recordings annotated by board certified veterinary cardiologists for quantitative evaluation. SoNUS version 3.2.x employs a multi-stage fallback architecture with quality-aware filtering to ensure reliable output under variable recording conditions. The primary sixty second model achieved mean and median heart rate accuracies of ninety one point six three percent and ninety four point nine five percent, while a fast model optimized for thirty to forty second recordings achieved mean and median accuracies of eighty eight point eight six percent and ninety two point nine eight percent. These results demonstrate the feasibility of extracting clinically relevant cardiac information from opportunistic smartphone recordings, supporting scalable preliminary assessment and telehealth applications in veterinary cardiology.

eess.SP