Semiautomated Three-Dimensional Landmark Placement on Knee Models Is a Reliable Method to Describe Bone Shape and Alignment by Nancy Park, B.S., Johannes Sieberer, M.Sc., Armita Manafzadeh, Ph.D., Rieke-Marie Hackbarth, Shelby Desroches, B.S., Rithvik Ghankot, B.S., John Lynch, Ph.D., Neil A. Segal, M.D., Joshua Stefanik, Ph.D., David Felson, M.D., John P. Fulkerson, M.D.

“…Purpose: To assess the inter- and intrarater reliability of 21 anatomical landmarks initially placed with an artificial intelligence algorithm and then manually verified with human input. …”
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Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning by Xiao Zeng, Qiong Ma, Chun-Xia Huang, Jun-Jie Xiao, Xi Fu, Yi-Feng Ren, Yu-Li Qu, Hong-Xia Xiang, Mao Lei, Ru-Yi Zheng, Yang Zhong, Ping Xiao, Xiang Zhuang, Feng-Ming You, Jia-Wei He

“…Seven advanced machine learning algorithms (logistic regression, support vector machine, multi-layer perceptron, naïve Bayes, random forest, gradient boosting decision tree, and LightGBM) were utilized to evaluate performance and identify key microorganisms, with fivefold cross-validation employed to ensure robustness. …”
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Comparative assessment of efficacy and toxicity of R-DA-EPOCH and R-mNHL-BFM-90 induction courses in the treatment of patients with diffuse large B-cell lymphoma with poor prognost... by M. O. Bagova, A. U. Magomedova, S. K. Kravchenko, Ya. K. Mangasarova, O. V. Margolin, E. S. Nesterova, L. G. Gorenkova, A. E. Misyurina, E. A. Fastova, F. E. Babaeva, K. A. Sychevskaya, S. M. Kulikov, Yu. A. Chabaeva, V. G. Savchenko

“…Despite the frequency of occurrence, there is no single algorithm for treating DLBCL patients with poor prognostic factors. …”
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Development of a Conditional Generative Adversarial Network Model for Television Spectrum Radio Environment Mapping by Oluwatobi Emmanuel Dare, Kennedy Okokpujie, Emmanuel Adetiba, Olabode Idowu-Bismark, Abdultaofeek Abayomi, Raymond Jules Kala, Emmanuel Owolabi, Udeme Christopher Ukpong

“…The model performance was evaluated using mean square error (MSE) and mean absolute error (MAE). 12 different experiments were carried out varying the training parameters of the CGAN architecture to obtain an optimal model. …”
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An Upscaling-Based Strategy to Improve the Ephemeral Gully Mapping Accuracy by Solmaz Fathololoumi, Daniel D. Saurette, Harnoordeep Singh Mann, Naoya Kadota, Hiteshkumar B. Vasava, Mojtaba Naeimi, Prasad Daggupati, Asim Biswas

“…Employing the Random Forest (RF) algorithm, this study executed three distinct strategies for EGs identification. …”
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Prediction of induction chemotherapy efficacy in patients with locally advanced nasopharyngeal carcinoma using habitat subregions derived from multi-modal MRI radiomics by Mulan Pan, Lu Lu, Xingyu Mu, Xingyu Mu, Guanqiao Jin

“…The K-means clustering algorithm was utilized to segment the tumor into five distinct habitat subregions based on imaging features. …”
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Predictive machine-learning model for screening iron deficiency without anaemia: a retrospective cohort study by Girish N Nadkarni, Orly Efros, Eyal Klang, Shelly Soffer, Gili Kenet, Aya Mudrik, Renana Robinson

“…The primary hypothesis was that an ML model could achieve better accuracy in identifying low ferritin levels (<30 ng/mL) in non-anaemic patients compared with traditional methods.Design A retrospective cohort study.Setting Data were derived from secondary and tertiary care facilities within the eight-hospital Mount Sinai Health System, an urban academic health system.Participants The study included 211 486 adult patients (aged ≥18 years) with normal haemoglobin levels (≥130 g/L for men and ≥120 g/L for women) and recorded ferritin measurements.Primary and secondary outcome measures The primary outcome was the prediction of low ferritin levels (<30 ng/mL) using extreme gradient-boosted decision trees, an ML algorithm suited for structured clinical data. Secondary outcomes included subgroup analyses stratified by sex and age to evaluate model performance in different populations.Data from 211 486 Mount Sinai Health System patients with normal haemoglobin levels and ferritin testing were analysed. …”
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Impact of Medical Conditions and Area Deprivation on Fundraising Success in Online Crowdfunding: Cross-Sectional Study by Steven S Doerstling, Matthew M Engelhard, Dennis Akrobetu, Caroline E Sloan, Ada Campagna, Thuy-Vi Nguyen, Farrah Madanay, Felicia Chen, Peter A Ubel

“…Previous studies have usually focused on a single disease category at a time or a small number of mutually exclusive diseases, even though a given campaign may seek funding for multiple conditions. In addition, differences in fundraising exist according to socioeconomic status, but whether this association applies across different diseases is unclear. …”
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Minimum carbon dioxide is a key predictor of the respiratory health of pigs in climate-controlled housing systems by Eddiemar Baguio Lagua, Hong-Seok Mun, Keiven Mark Bigtasin Ampode, Hae-Rang Park, Md Sharifuzzaman, Md Kamrul Hasan, Young-Hwa Kim, Chul-Ju Yang

“…However, maintaining air quality is a limitation of current housing systems. This study evaluated the growth and health parameters of pigs raised under different environmental conditions and identified key environmental variables that determine respiratory health. …”
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Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach by Yang Chen, Xiaojie Su, Fei Yao, Yushan Liu, Hua Xing, Yubin Ju, Zhiran Kang, Wuquan Sun, Lijun Yao, Li Gong

“…The total reliability of the scale is 0.94 and the KMO (Kaiser-Meyer-Olkin measure of sampling adequacy) value of the scale validity was 0.949 (>0.7). The CatBoost algorithm-based machine-learning model achieved the best predictive performance in distinguishing practitioners with different degrees of knee pain after Tai Chi practice. …”
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Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study by Yelong Shen, Siyu Wu, Yanan Wu, Chao Cui, Haiou Li, Shuang Yang, Xuejun Liu, Xingzhi Chen, Chencui Huang, Ximing Wang

“…The radiomics features were extracted respectively, and the features were screened by machine learning algorithm and statistical method. Radiomics models of seven different sequence permutations were constructed. …”
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Interpersonal counselling for adolescent depression delivered by youth mental health workers without core professional training: the ICALM feasibility RCT by Jon Wilson, Viktoria Cestaro, Eirini Charami-Roupa, Timothy Clarke, Aoife Dunne, Brioney Gee, Sharon Jarrett, Thando Katangwe-Chigamba, Andrew Laphan, Susie McIvor, Richard Meiser-Stedman, Jamie Murdoch, Thomas Rhodes, Carys Seeley, Lee Shepstone, David Turner, Paul Wilkinson

“…Aims The aims of this feasibility study were to (1) assess the feasibility and acceptability of trial procedures, (2) explore the delivery of IPC-A and treatment as usual (TAU) and how and why intervention delivery varies across differing service contexts, (3) evaluate the extent of contamination of the control arm and if it should be mitigated against in a future trial and (4) investigate if the interval estimate of benefit of IPC over TAU in depression scores post treatment includes a clinically significant effect. …”
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Comparison between clinician and machine learning prediction in a randomized controlled trial for nonsuicidal self-injury by Moa Pontén, Oskar Flygare, Martin Bellander, Moa Karemyr, Jannike Nilbrink, Clara Hellner, Olivia Ojala, Johan Bjureberg

“…The aim of this study was to explore clinician predictions of which adolescents would abstain from nonsuicidal self-injury after treatment as well as how these predictions match machine-learning algorithm predictions. Methods Data from a recent trial evaluating an internet-delivered emotion regulation therapy for adolescents with nonsuicidal self-injury was used. …”
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Research on leaf identification of table grape varieties based on deep learning by PAN Bowen, LIN Meiling, JU Yanlun, SU Baofeng, SUN Lei, FAN Xiucai, ZHANG Ying, ZHANG Yonghui, LIU Chonghuai, JIANG Jianfu, FANG Yulin

“…[Conclusion] The ResNet-101 model had the highest overall recognition accuracy, the lowest LOSS value, the higher average recognition accuracy and Recall rate of varieties, and could get a better model with fewer iterations, which would take less time. The Grad-CAM algorithm was used to evaluate the classification effect of four convolutional neural networks, and all of them could accurately identify the main features of the leaves. …”
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LLM in the Loop: A Framework for Contextualizing Counterfactual Segment Perturbations in Point Clouds by Veljka Kocic, Niko Lukac, Dzemail Rozajac, Stefan Schweng, Christoph Gollob, Arne Nothdurft, Karl Stampfer, Javier del Ser, Andreas Holzinger

“…The proposed framework undergoes rigorous evaluation, combining human inspection of LLM-generated suggestions with quantitative analysis of semantic classification model performance across different LLM variants. …”
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A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer by Sushant Patkar, Stephanie Harmon, Isabell Sesterhenn, Rosina Lis, Maria Merino, Denise Young, G. Thomas Brown, Kimberly M. Greenfield, John D. McGeeney, Sally Elsamanoudi, Shyh-Han Tan, Cara Schafer, Jiji Jiang, Gyorgy Petrovics, Albert Dobi, Francisco J. Rentas, Peter A. Pinto, Gregory T. Chesnut, Peter Choyke, Baris Turkbey, Joel T. Moncur

“…This strategy resulted in improved model generalizability in the test set compared with three different control experiments when evaluated on both needle biopsy slides and whole-mount prostate slides from different centers. …”
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Toward Digital Twin of Off-Road Vehicles Using Robot Simulation Frameworks by Arianna Rana, Antonio Petitti, Angelo Ugenti, Rocco Galati, Giulio Reina, Annalisa Milella

“…Research presented in this paper deals with the development of the digital version of off-road vehicles. Two different robot simulation frameworks are investigated. …”
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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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Epidemiology and clinical features of psoriasis in hard-to-treat body locations: a Chinese nationwide population-based study by Lingyi Lu, Lu Cao, Fan Jiang, Sihan Wang, Yingzhe Yu, Hua Huang, Bingjiang Lin

“…These findings support incorporating hard-to-treat area evaluation into psoriasis severity assessments and treatment algorithms.…”
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Machine learning in lymphocyte and immune biomarker analysis for childhood thyroid diseases in China by Ruizhe Yang, Wei Li, Qing Niu, WenTao Yang, Wei Gu, Xu Wang

“…Additionally, nine machine learning (ML) algorithms were utilized to construct prediction models. …”
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