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Published on: August 9, 2024
Diagnostic Performance Evaluation of Clinical and Artificial Intelligence Risk Models in Patients Referred for
Armin Garmany1, Jose K James2, Gregorio Tersalvi2
1Medical Scientist Training Program, Mayo Clinic, Rochester, Minnesota.
Summary
Artificial intelligence echocardiography (AI-Echo) shows superior diagnostic ability for cardiac amyloidosis (CA) compared to AI electrocardiography (AI-ECG) and the ATTR-CM score. AI-Echo can help reduce unnecessary screening tests for CA.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Screening for cardiac amyloidosis (CA) is crucial for early diagnosis and treatment.
- Various artificial intelligence (AI) and statistical models exist, but comparative data on their utility is limited.
- Optimizing diagnostic algorithms for CA requires understanding the relative performance of these tools.
Purpose of the Study:
- To compare the diagnostic performance of AI-Echo, AI-ECG, and the ATTR-CM clinical score for transthyretin CA (ATTR-CA).
- To evaluate the clinical utility of these models in reducing unnecessary diagnostic procedures.
Main Methods:
- Retrospective cohort study including 598 patients referred for cardiac scintigraphy.
- Compared AI-Echo, AI-ECG, and ATTR-CM score performance using previously defined thresholds.
- Excluded 28 patients with AL amyloidosis for ATTR-CM score validation.
Main Results:
- AI-Echo demonstrated 86% sensitivity and 85% specificity for ATTR-CA.
- AI-Echo achieved an AUROC of 0.93, outperforming AI-ECG (0.79) and ATTR-CM score (0.87).
- AI-Echo could potentially avoid more unnecessary scintigraphy (45 per 100) compared to AI-ECG (24) and ATTR-CM score (37).
Conclusions:
- AI-Echo exhibits superior diagnostic discrimination for ATTR-CA in high-risk populations.
- AI-Echo offers greater clinical utility in identifying ATTR-CA compared to AI-ECG and the ATTR-CM score.
- AI-Echo may optimize diagnostic pathways and reduce healthcare costs associated with CA screening.
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