Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learn... by Ting Ge, Guixin He, Qian Cui, Shuangcui Wang, Zekun Wang, Yingying Xie, Yuanyuan Tian, Juyue Zhou, Jianchun Yu, Jinmin Hu, Wentao Li

“…Subsequently, we developed a novel machine learning framework that incorporated 12 machine learning algorithms and their 113 combinations to construct a LUAD CS-related signature (LUAD-CSRS), which were assessed in both training and validation cohorts. …”
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Machine learning for synchronous bone metastasis risk prediction in high grade lung neuroendocrine carcinoma by Bo Lan, Zongyun He, Zhe Chen, Haibing Tao, Tao Liu, Jin Yang

“…All patients were randomly divided into the training cohort and validation cohort (8:2). Eight machine learning algorithms were used to construct predictive model for synchronous BM in the training cohort, and the optimal model was selected for further validation. …”
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Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis by Man Sun, Jianzhang Wang, Ping Xu, Libo Zhu, Gen Zou, Shuyi Chen, Yuanmeng Liu, Xinmei Zhang

“…The relationship between magnetic resonance imaging (MRI) feature and symptom remains unclear.Objective To evaluate the performance of MRI radiomics model for differentiating symptom heterogeneity of extrinsic adenomyosis, pain, abnormal uterine bleeding (AUB), infertility, and no symptom.Materials and methods This retrospective analysis included 405 patients with MRI-diagnosed extrinsic adenomyosis (January 2020-July 2022), randomly split 7:3 into training and test cohorts. …”
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Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study by Emanuele Frassini, Teddy S. Vijfvinkel, Rick M. Butler, Maarten van der Elst, Benno H. W. Hendriks, John J. van den Dobbelsteen

“…An ensemble model derived by averaging the two best performing algorithms reported low MAE and SMAPE, although needing longer training. …”
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Late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligence by Omer Burak Demirel, Fahime Ghanbari, Christopher W. Hoeger, Connie W. Tsao, Adele Carty, Long H. Ngo, Patrick Pierce, Scott Johnson, Kathryn Arcand, Jordan Street, Jennifer Rodriguez, Tess E. Wallace, Kelvin Chow, Warren J. Manning, Reza Nezafat

“…In this study, we sought to evaluate a rapid two-dimensional (2D) LGE imaging protocol using a generative artificial intelligence (AI) algorithm with inline reconstruction. …”
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Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospec... by Kohji Nishida, Ryo Kawasaki, Yiming Qian, Liangzhi Li, Yuta Nakashima, Hajime Nagahara

“…In MACE prediction, our model outperformed the traditional score-based models, with 8.2% higher AUC than Systematic COronary Risk Evaluation (SCORE), 3.5% for SCORE 2 and 7.1% for the Framingham Risk Score (with p value<0.05 for all three comparisons).Conclusions Our algorithm estimates the 5-year risk of MACE based on retinal images, while explicitly presenting which risk factors should be checked and intervened. …”
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Nitrogen content estimation of apple trees based on simulated satellite remote sensing data by Meixuan Li, Xicun Zhu, Xicun Zhu, Xinyang Yu, Cheng Li, Dongyun Xu, Ling Wang, Dong Lv, Yuyang Ma

“…The support vector machine model constructed based on Sentinel-2 satellite simulated data was the optimal nitrogen content inversion model, with an average R² value of 0.81 and an average RMSE value of 0.15 for training sets across different phenological periods, and an average R² value of 0.61 and an average RMSE value of 0.23 for validation sets.DiscussionThis study systematically evaluated the applicability and accuracy differences of multi-source satellite data for estimating nitrogen content in apple trees, and clarified the variation patterns of nitrogen-sensitive spectral bands and optimal modeling strategies across key phenological stages. …”
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Multimodal AI for Home Wound Patient Referral Decisions From Images With Specialist Annotations by Reza Saadati Fard, Emmanuel Agu, Palawat Busaranuvong, Deepak Kumar, Shefalika Gautam, Bengisu Tulu, Diane Strong

“…To overcome the challenges posed by a small and imbalanced dataset, DM-WAT integrates image and text augmentation along with transfer learning via pre-trained feature extractors to achieve high performance. …”
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Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records by Noorul Husna Abd Rahman, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Ayman Khallel Al-Ani, D. Jerline Sheebha Anni, Prabu Mohandas

“…Then, four machine learning algorithms and three deep learning networks are evaluated to determine the best predictive model. …”
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Factors influencing the response to periodontal therapy in patients with diabetes: post hoc analysis of a randomized clinical trial using machine learning by Nidia CASTRO DOS SANTOS, Arthur MANGUSSI, Tiago RIBEIRO, Rafael Nascimento de Brito SILVA, Mauro Pedrine SANTAMARIA, Magda FERES, Thomas VAN DYKE, Ana Carolina LORENA

“…A leave-one-out cross-validation strategy was used for model training and evaluation. We tested seven different algorithms: K-Nearest Neighbors, Decision Tree, Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Logistic Regression. …”
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Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients by Xiaoyan Yin, Yongbin Cui, Tonghai Liu, Zhenjiang Li, Huiling Liu, Xingmin Ma, Xue Sha, Changsheng Ma, Dali Han, Yong Yin

“…Patients were randomly divided into training cohort and validation cohort with a ratio of 7:3. …”
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A safe-enhanced fully closed-loop artificial pancreas controller based on deep reinforcement learning. by Yan Feng Zhao, Jun Kit Chaw, Mei Choo Ang, Yiqi Tew, Xiao Yang Shi, Lin Liu, Xiang Cheng

“…It employed ten tricks to enhance the proximal policy optimization (PPO) algorithm, improving training efficiency. Additionally, a dual safety mechanism of 'proactive guidance + reactive correction' was introduced to reduce the risks of hyperglycemia and hypoglycemia and to prevent emergencies. …”
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ADeepWeeD: An adaptive deep learning framework for weed species classification by Md Geaur Rahman, Md Anisur Rahman, Mohammad Zavid Parvez, Md Anwarul Kaium Patwary, Tofael Ahamed, David A. Fleming-Muñoz, Saad Aloteibi, Mohammad Ali Moni, PhD

“…Existing DL-based weed classification techniques, including VGG16 and ResNet50, initially construct a model by implementing the algorithm on a training dataset comprising weed species, subsequently employing the model to identify weed species acquired during training. …”
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Building radiomics models based on ACR TI-RADS combining clinical features for discriminating benign and malignant thyroid nodules by Xingxing Chen, Xingxing Chen, Lili Zhang, Bin Chen, Jiajia Lu

“…PurposeThe aim of this study was to establish and validate a radiomics model combining the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) and clinical features and to build a nomogram that could be utilized to enhance the diagnostic performance of malignant thyroid nodules.MethodFrom January 2019 to September 2022, 329 thyroid nodules from 323 patients who had been referred for surgery and had pathological evidence of them were gathered retrospectively and randomly allocated to training and test cohorts (8:2 ratio). A total of 107 radiomics features were extracted from the US images, and the radiomics score (Rad-score) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. …”
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Detecting severe coronary artery stenosis in T2DM patients with NAFLD using cardiac fat radiomics-based machine learning by Mengjie Liang, Liting Fang, Xie Chen, Wendi Huang

“…The detection performance of these models was subsequently evaluated in both the training and validation cohorts. …”
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Research on adversarial attacks and defense performance of image classification models for automated driving systems by TANG Jun, HUANG Wenjing, LI Shuang, WU Zili

“…Then, sensitivity analyses were carried out across different regions and examples to identify attack mechanisms using three adversarial interpretation algorithms: LRP, Grad-CAM, and LIME. Based on these analysis results, optimization algorithms such as swarm intelligence defense and adversarial training were adopted to verify the classification performance of the model following adversarial training. …”
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Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction by Jiahui Lai, Cailian Cheng, Tiantian Liang, Leile Tang, Xinhua Guo, Xun Liu

“…The extreme gradient boosting (XGBoost) algorithm exhibited superior performance across training (AUC 0.963, 95% CI: 0.951–0.975), internal validation (AUC 0.940, 95% CI: 0.924–0.956), and external validation (AUC 0.850, 95% CI: 0.832–0.868) datasets. …”
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Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models by Youzhi Lian, Yinyu Shi, Haibin Shang, Hongsheng Zhan

“…Five-fold cross-validation was employed to ensure robust model evaluation and minimize overfitting. In each fold, the dataset was split into training and validation sets, with model performance assessed using multiple metrics including accuracy, precision, recall, and F1 score. …”
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Multi-label semantic segmentation of magnetic resonance images of the prostate gland by Mark Locherer, Christopher Bonenberger, Wolfgang Ertel, Boris Hadaschik, Kristina Stumm, Markus Schneider, Jan Philipp Radtke

“…To increase data variety for training and evaluation we use image augmentation on our two datasets—a freely available dataset and our new open-source dataset. …”
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StrideHD: A Binary Hyperdimensional Computing System Utilizing Window Striding for Image Classification by Dehua Liang, Jun Shiomi, Noriyuki Miura, Hiromitsu Awano

“…StrideHD encodes data points to distributed binary hypervectors and eliminates the expensive Channel item Memory (CiM) and item Memory (iM) in the encoder, which significantly reduces the required hardware cost for inference. Our evaluation also shows that compared with two popular HD algorithms, the singlepass StrideHD model achieves a 27.6<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> and 8.2<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> reduction in inference memory cost without hurting the classification accuracy, while the iterative mode further provides 8.7<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> memory efficiency. …”
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