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
Related News
•Radiology Business
Real-World Study: Radiology AI Best in Emergency and Inpatient Settings
A commercial AI tool for intracranial aneurysm detection outperformed in inpatient and emergency settings but yielded limited benefits for outpatients in a major health system study.
•Radiology Business
New Rubric Enhances Safety of AI-Generated Radiology Summaries
Researchers developed a five-factor rubric to assess the safety and quality of AI-generated, patient-friendly radiology report summaries.
•Radiology Business
Harrison.ai Restructures and Launches U.S. AI-Enabled Teleradiology Venture
Harrison.ai lays off staff amid major restructuring and launches Frontier Radiology, an AI-powered U.S. teleradiology business.