An AI model achieved high accuracy in identifying esophageal achalasia on chest x-rays, surpassing physician performance.
Key Details
- 1Deep-learning AI model trained using 207 x-rays from 144 achalasia patients and 240 controls.
- 2Validation performed on a separate test set: 17 achalasia and 64 control x-rays.
- 3Model achieved AUC of 0.964, sensitivity 0.941, and specificity 0.891.
- 4Outperformed four physicians on the same test cases.
- 5Achalasia is a rare disorder with diagnosis often delayed by 6.5 years on average.
- 6Noninvasive screening using existing chest x-ray data may enable earlier diagnosis.
Why It Matters
Utilizing routine chest x-rays and AI, clinicians could screen for achalasia in a noninvasive way, potentially reducing diagnostic delays and improving patient outcomes. This highlights AI's growing role in enhancing radiological workflows.
Source
AuntMinnie
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