This systematic review and meta‑analysis examined 48 machine‑learning studies that predict live‑birth outcomes after IVF/ICSI using routine clinical data. The pooled discrimination of internally validated models was an AUC of 0.814, while externally validated models had an AUC of 0.756, with no significant difference between them. Most studies (47/48) were at high or unclear risk of bias, only four assessed calibration, and none demonstrated clinical impact, leading to a low‑to‑very‑low certainty of evidence.
By Abdul Raheem Shahzad, Muhammad Zaheer Khan, Umar Ajaib, Asad Ur Rehman, Juan Deng, Ming Zhang
arXiv:2606. 12006v1 Announce Type: cross Abstract: Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis.
By Minh-Khoi Pham, Luca Cotugno, Alina Sirbu, Tai Tan Mai, Martin Crane, Marija Bezbradica
FETALFusion is a dual‑path CNN‑Mamba encoder‑decoder designed for fetal head segmentation in 2D ultrasound across transabdominal and transperineal acquisition types. It uses four‑directional state‑space scans at the highest‑resolution stages, domain‑specific channel gates, and a weak landmark shape prior, achieving a Dice score of 0.963 and an HD95 of 2.10 mm on a held‑out test set of 471 images. Ablation studies show that the landmark prior and domain gates drive most of the performance gains, while the state‑space branch has minimal impact.
whyItMatters":"The method provides highly accurate fetal head segmentation without test‑time augmentation or domain labels, enabling reliable head circumference measurement for monitoring fetal growth."
By Ammar Bhilwarawala, Mainak Bandyopadhyay
arXiv:2607. 11656v1 Announce Type: cross Abstract: Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data.
By Christelle Schneuwly Diaz, Narmina Baghirova, Duy-Thanh Vu, Duy-Cat Can, Gilles Allali, Philippe Ryvlin, Oliver Y. Ch\'en
arXiv:2607. 26752v1 Announce Type: new Abstract: Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under interventions, supporting downstream tasks from imaging-based diagnosis to digital-twin treatment planning.
By Behraj Khan, Shabir Ahmad, Syed Ahmad Chan Bukhari, Tahir Qasim Syed
The paper introduces a dual cross‑attention deep learning framework for automated grading of Cervical Intraepithelial Neoplasia (CIN) and prediction of Swede scores, using a newly released BUET Multi‑Center Colposcopy Dataset. The architecture fuses paired multimodal cervigrams and employs a custom composite loss to handle class imbalance, achieving 71.85% accuracy and 86.23% AUC‑ROC for three‑class CIN grading, and AUC‑ROC values between 75.7% and 88.4% for individual Swede score components. The total predicted Swede Score has a mean absolute error of 1.489, indicating potential for AI‑assisted colposcopy screening in resource‑limited settings.
By Dania Khan, Nuzhat Aisha Shaikh, Asfina Hassan Juicy, Raiyun Kabir, S M Shahida, Taufiq Hasan