ArticleInternational journal of women's health2025
Interobserver Variability in Hysterosalpingography Interpretation: Radiologists vs Gynecologists.
Article in International journal of women's health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Interobserver Agreement Between Artificial Intelligence, Radiologist, and Gynecologist in Hysterosalpingography Interpretation: A Retrospective Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
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Authors and funding
15 authors.
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Abstract
Objective: To evaluate interobserver variability in the interpretation of hysterosalpingography (HSG) examinations among radiologists, gynecologists, and between the two specialties, highlighting areas of diagnostic agreement and discrepancy. Materials and Methods: In this prospective, multicenter study, 12 specialists (6 radiologists and 6 gynecologists) independently reviewed HSG images from 100 patients, evaluating 10 predefined diagnostic categories and overall image quality. Fleiss' kappa (κ) was used to assess interobserver agreement within and between groups. Results: Interobserver agreement ranged from poor to moderate across most diagnostic categories. The highest agreement was observed for tubal occlusion (κ = 0.508 radiologists vs gynecologists; κ = 0.536 among radiologists; κ = 0.460 among gynecologists), followed by uterine anomalies and hydrosalpinx. Poor agreement was noted for subjective parameters such as image quality and additional findings (κ values < 0.1), with some negative kappa scores indicating agreement below chance. Conclusion: Significant interobserver variability exists in HSG interpretation, particularly between radiologists and gynecologists. Structured findings yielded higher agreement, while subjective assessments showed poor reproducibility. These findings underscore the need for standardized reporting guidelines, interdisciplinary collaboration, and the potential integration of AI-assisted interpretation to enhance diagnostic consistency and improve patient outcomes.
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