ReviewAbdominal radiology (New York)2026
Artificial intelligence potential in ovarian endometriosis imaging: a comparative meta-analysis of transvaginal ultrasound-based AI models and human readers.
Review in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Beyond Imaging Response: Have We Chosen the Right Endpoint After Transarterial Chemoembolization?Cardiovascular and interventional radiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTransvaginal ultrasound (TVUS) is widely used for diagnosing ovarian endometriosis but remains limited by significant operator dependency. This systematic review and meta-analysis evaluated the diagnostic accuracy of ultrasound-based artificial intelligence (AI) models for ovarian endometriosis and directly compared their performance with that of human readers.
methodsWe conducted a comprehensive search of five databases (PubMed, Embase, Scopus, Web of Science, and Cochrane Library) up to 5 December 2025 to identify studies reporting diagnostic metrics for AI models, compared with human readers, for detecting ovarian endometriomas. Pooled sensitivity, specificity, and area under the curve (AUC) were calculated using a bivariate random-effects model.
resultsSeven studies with 2737 patients (6061 images) were included. AI models demonstrated a pooled sensitivity of 91% (confidence interval 81%-96%) and specificity of 95% (confidence interval 92%-96%). Human readers achieved a pooled sensitivity of 80% (95% confidence interval 65-90) and specificity of 85% (95% confidence interval 74-92). AI models significantly outperformed readers in specificity (p = 0.001) and overall diagnostic discrimination, with an AUC of 0.97 (95% confidence interval 0.95-0.98) compared with 0.84 (95% confidence interval 0.82-0.88) for human readers (p < 0.001), while sensitivity remained comparable (p = 0.10). Heterogeneity across included studies was minimal (0%).
conclusionAI algorithms show indications of promising but preliminary diagnostic performance relative to human readers in specificity and overall discrimination metrics. These results raise the possibility that AI might serve as a supplementary tool in sonographic evaluation of ovarian endometriosis, potentially contributing to more standardized interpretation and reduced false-positive diagnoses.
Indexed as
Identifiers
42189210What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.