ArticleFundamental research2026
Development of a one-step office hysteroscopic lightweight artificial intelligence application for subfertility risk stratification of intrauterine adhesions.
Article in Fundamental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05381376 (Multicentre Prospective Clinical Database for the Construction of Predictive Models on Risk of Intrauterine Adhesion), which is not on this map. Cited by 3 papers.
What it found
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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.
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.
Multicentre Prospective Clinical Database for the Construction of Predictive Models on Risk of Intrauterine Adhesion
Who cites it
3 citing papers in PubMed.
- Artificial Intelligence, Deep Learning, and Computer Vision in Hysteroscopy: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in echocardiography: Applications and future directions.Fundamental research · 2026Review
- Visualized hysteroscopic artificial intelligence fertility assessment system for endometrial injury: an image-deep-learning study.Annals of medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intrauterine adhesions (IUAs) pose challenges to natural fertility and impede timely conception. Assisted reproductive technologies (ART) offer effective solutions but are often expensive in developing nations. This study introduces an artificial intelligence (AI) application, MobilenetV3PH, featuring a proportional hazard (PH) neural network architecture to predict reproductive outcomes following hysteroscopic adhesiolysis in IUAs and to categorize subfertility risks. The present study leveraged 4922 second-look hysteroscopic images obtained from 555 patients post-hysteroscopic adhesiolysis, sourced from a prospective IUA clinical database (NCT05381376). The prospective cohort was randomly partitioned into training, validation, and test sets for model development, hyperparameter validation, and external validation. MobilenetV3PH achieved a noteworthy Area Under the Curve (AUC) of 0.920, outperforming alternative models significantly. The AI application seamlessly integrated into the hysteroscopic platform, demonstrating an average analysis time of 35.7 ± 5.6 s per patient. Notably, it excelled in predicting natural conception compared to conventional clinical scoring methods. The identification of high-risk IUAs patients who encounter difficulties in natural conception through AI-predicted subfertility risks revealed that these individuals might derive greater benefits from ART (HR = 4.616,
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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.