Results 221 to 230 of about 67,939 (291)

Identifying Venous Insufficiency in Head and Neck Reconstruction Flaps Using Machine Learning and Deep Learning Methods

Head &Neck, EarlyView.
ABSTRACT Background Venous insufficiency is a major cause of flap failure in head and neck reconstruction. AI provides a reliable, convenient solution for early detection. Methods Clinical data and postoperative flap photos of head and neck cancer patients (2018–2024) at our center were retrospectively collected, categorized into normal and venous ...
Yurong He, Jugao Fang, Lizhen Hou, Zufei Li, Shizhi He, Yanming Zhao, Qijia Li, Rongjia Li, Xiaoyu Shi, Jiayi Yang, Qi Zhong   +10 more
wiley   +1 more source

Data-driven machine learning models for predicting engineering properties in deep-sea sediments. [PDF]

Sci Rep
Yun J, Park J, Choo H, Lee HM, Won J.
europepmc   +1 more source

Machine learning‐based prediction of large‐for‐gestational‐age neonates in diabetic and non‐diabetic pregnancies

International Journal of Gynecology &Obstetrics, EarlyView.
Abstract Objective This study determines whether a machine‐learning model integrating sonographic biometry with maternal clinical parameters improves prediction of large‐for‐gestational‐age (LGA) compared with Hadlock's EFW formula. Methods We conducted a retrospective cohort study including all singleton live births at ≥32 gestational weeks at a ...
Ohad Houri, Asaf Romano, Asnat Walfisch, Eran Hadar, Yinon Gilboa, Leor Perl, Nadav Loebl, Ron Unger   +7 more
wiley   +1 more source

Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers. [PDF]

J Neural Eng
Cano Achuri MI, Lara MK, Abed Rabbo K, Wilson BT, Meek A, Mahoney JM, Hernan AE, Brockmeier AJ.   +7 more
europepmc   +1 more source

Interpretable machine learning enables early and accurate detection of drug‐induced liver injury: A multicenter study with real‐world clinical translation

Interdisciplinary Medicine, EarlyView.
This study develops an interpretable gradient‐boosting model that accurately identifies drug‐induced liver injury (DILI) using routine laboratory data. The model explains key clinical features through SHapley Additive exPlanations analysis and detects DILI earlier than expert evaluation, offering a transparent and practical tool for precision ...
Jingyi Ling, Shian Zhang, Jun Lin, Zhengju Xu, Runcheng Xu, Qian Lv, Kaixiang Zhang, Sheng Liu, Jie Guo, Cheng Hua, Yin Jia, Xiaoyu Xu, Kun Qian, Shanrong Liu   +13 more
wiley   +1 more source

An interpretable machine learning model using SHapley Additive exPlanations for preoperative cervical lymph node metastasis risk stratification in tongue squamous cell carcinoma: a multicenter study. [PDF]

BMC Oral Health
Li Y, Huang N, Wang L, Xiao H, Chen W, Xing Y, Nishioka T, Zhou K, Kojima I, Chen J, Ou Y, Li W.   +11 more
europepmc   +1 more source

Decoding temporal miRNA signatures of semen under in vitro exposure for forensic time since deposition estimation using machine learning‐driven modeling

Interdisciplinary Medicine, EarlyView.
This study develops a novel miRNA‐based framework for estimating the time since deposition of semen stains, combining small RNA sequencing with machine learning. Time‐dependent miRNA modules were identified using Mfuzz clustering and WGCNA, followed by a multi‐stage feature selection pipeline that reduced 261 candidate miRNAs to a minimal 7‐miRNA panel.
Meiming Cai, Qiong Lan, Tong Xie, Qinglin Liu, Ming Zhao, Xiaolian Wu, Xin Shi, Ruonan Shen, Yiman Wu, Chen Mao, Bin Cong, Bofeng Zhu   +11 more
wiley   +1 more source

Gaining Brain Insights by Tapping into the Black Box: Linking Structural MRI Features to Age and Cognition using Shapley-Based Interpretation Methods. [PDF]

Neuroinformatics
Kropiunig J, Sørensen Ø.
europepmc   +1 more source

Magnetocardiography combined with machine learning for pulmonary hypertension detection and improved short‐term risk assessment

Interdisciplinary Medicine, EarlyView.
Magnetocardiography (MCG) enables non‐invasive mapping of cardiac magnetic fields. In this study, an MCG‐based machine learning model detects pulmonary hypertension with robust performance. Furthermore, MCG features may improve the accuracy of short‐term risk assessment.
Yuankun Qi, Jiaqi Liang, Kai Ma, Xiaoyu Hu, Yu Zhang, Xiaole Han, Dong Xu, Xu Zhang, Xiaopei Cui, Min Xiang, Hongyu Zhang, Jiancheng Fang   +11 more
wiley   +1 more source

Migrating in a Warming World: A Deep Learning Approach to Predict Pan-American Seasonal Shifts in the Monarch Butterfly Niche. [PDF]

Glob Chang Biol
Vanalli C, Zbinden R, van Tiel N, Tuia D.   +3 more
europepmc   +1 more source