Future cardiology · 2026
AI-Assisted MI diagnosis from echocardiogram videos: does explainability enhance human-AI collaborative accuracy?
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Khan Aaisha, Islam Iman, Mannakkara Nilanka, Noakes Daniela, Mansour Ahmed, Davies Lia, Marcolin Cecelia, Aquilina Anneslise, Dignazi Federico, Parsons Samuel, King Andrew, Day Thomas G
Аннотация
BACKGROUND: Echocardiography after myocardial infarction (MI) provides clinically useful information through assessment of regional wall motion abnormalities, but interpretation requires expertise and remains subject to observer variability. Artificial intelligence (AI) shows promise in automatic interpretation, but it is unclear how explainability affects human-AI collaborative performance.
METHODS: A ResNet18-LSTM model was trained to classify normal vs MI on 127 apical four chamber (A4C) and 120 apical two chamber (A2C) echocardiogram videos from the HMC-QU dataset. Gradient-weighted Class Activation Mapping (Grad-CAM provided visual explanations. Eight cardiology trainees compared diagnostic performance across three conditions: (a) echo clips alone, (b) echo clips with AI predictions, and (c) echo clips with AI predictions plus Grad-CAM explanations.
RESULTS: The AI models demonstrated strong discriminative performance with AUCs of 0.9429 (A2C) and 0.9250 (A4C). AI alone achieved 80.0% accuracy versus 77.0% for clinicians alone. Surprisingly, combining AI with human judgment did not improve performance, and introducing visual explanations reduced accuracy to 72% and specificity from 93.8% to 83.8% (p = 0.046).
CONCLUSION: While AI models can effectively detect MI on echocardiographic videos, current explainability techniques may misalign with clinical reasoning, potentially impairing diagnostic performance. Future integration requires AI visual explanation strategies that complement clinician expertise.
METHODS: A ResNet18-LSTM model was trained to classify normal vs MI on 127 apical four chamber (A4C) and 120 apical two chamber (A2C) echocardiogram videos from the HMC-QU dataset. Gradient-weighted Class Activation Mapping (Grad-CAM provided visual explanations. Eight cardiology trainees compared diagnostic performance across three conditions: (a) echo clips alone, (b) echo clips with AI predictions, and (c) echo clips with AI predictions plus Grad-CAM explanations.
RESULTS: The AI models demonstrated strong discriminative performance with AUCs of 0.9429 (A2C) and 0.9250 (A4C). AI alone achieved 80.0% accuracy versus 77.0% for clinicians alone. Surprisingly, combining AI with human judgment did not improve performance, and introducing visual explanations reduced accuracy to 72% and specificity from 93.8% to 83.8% (p = 0.046).
CONCLUSION: While AI models can effectively detect MI on echocardiographic videos, current explainability techniques may misalign with clinical reasoning, potentially impairing diagnostic performance. Future integration requires AI visual explanation strategies that complement clinician expertise.