Bridging the Gap Between Artificial Intelligence and Clinical Readiness in Endometriosis Diagnosis: A Systematic Review.
Martina Haber, Matthew Montebello, Gian Paul Gauci et al.
✦ AI-curated · Sources linked
In 30 seconds
This systematic review evaluated the diagnostic performance of artificial intelligence (AI) applications in endometriosis diagnosis, focusing on imaging and clinical symptoms. The review found that imaging-based algorithms achieved diagnostic accuracies up to 94.32% for MRI and 95.95% for symptom-based models. However, significant clinical heterogeneity and biases were noted, limiting the applicability of these findings.
Key findings
- Imaging algorithms achieved diagnostic accuracies up to 94.32% for MRI.
- Symptom-based models reported accuracies reaching 95.95% using classifiers like Random Forest.
- Significant clinical heterogeneity and systemic vulnerabilities were identified in the studies.
- Most models were trained on advanced-stage cohorts, limiting early-stage detection applicability.
Why it matters
Understanding the potential and limitations of AI in diagnosing endometriosis is crucial for clinicians. It highlights the need for robust validation studies to ensure these technologies can be effectively integrated into clinical practice.
What to keep in mind
The review noted significant clinical heterogeneity and systemic vulnerabilities, with most models trained on advanced-stage cohorts.