Generic health status improves after non-surgically and surgically treated hip abductor tendon pathology: a retrospective study of ninety-seven female patients by Nickan Zakikhany, Jeppe Lange, Bent Lund, Marie B. Bohn

“…Females aged 18+ years, with positive clinical tests and MRI verified hip abductor tendon pathology, who had completed the EQ-5D-5L at relevant timepoints, were included. Our treatment algorithm consisted of a baseline physiotherapist led intervention (patient education followed by 3-months un-supervised training). …”
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Analysis of Factors Influencing the Mental Health Status of Personnel Stationed at High- and Low-Altitude Bases by Li HF, Chen J, Ge YF, Liu SJ, Zhou LJ, Dong GG

“…Principal component analysis (PCA) based on a Random Forest algorithm was employed to evaluate psychological symptom patterns.Results: The mental health status of personnel included in this study surpassed the national average for China, with personnel stationed at high-altitude bases reporting better overall mental health than those stationed at low-altitude bases. …”
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Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study by Chanmin Park, Changho Han, Su Kyeong Jang, Hyungjun Kim, Sora Kim, Byung Hee Kang, Kyoungwon Jung, Dukyong Yoon

“…External validation was performed using data from 670 patients at Ajou University Hospital (March 2022 to September 2022). We evaluated machine learning algorithms (random forest [RF], extra-trees classifier, and light gradient boosting machine) and selected the RF model as the final model based on its performance. …”
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Machine Learning Models for Predicting Abnormal Brain CT Scan Findings in Mild Traumatic Brain Injury Patients by Amirmohammad Toloui, Amir Ghaffari Jolfayi, Hamed Zarei, Arash Ansarian, Amir Azimi, Seyed Mohammad Forouzannia, Rosita Khatamian Oskooi, Gholamreza Faridaalaee, Shayan Roshdi Dizaji, Seyed Ali Forouzannia, Seyedeh Niloufar Rafiei Alavi, Mohammadreza Alizadeh, Hadis Najafimehr, Saeed Safari, Alireza Baratloo, Mostafa Hosseini, Mahmoud Yousefifard

“…The dataset was split 80:20 for training/testing, and multiple ML algorithms were evaluated using accuracy, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). …”
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Predicting the risk of postoperative gastrointestinal bleeding in patients with Type A aortic dissection based on an interpretable machine learning model by Lin Li, Xing Yang, Wei Guo, Wenxian Wu, Meixia Guo, Huanhuan Li, Xueyan Wang, Siyu Che

“…The dataset was divided into training and validation sets in a 7:3 ratio. Predictive performance was evaluated and compared using Receiver Operating Characteristic (ROC) curves and DeLong tests. …”
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Screening Model for Bladder Cancer Early Detection With Serum miRNAs Based on Machine Learning: A Mixed‐Cohort Study Based on 16,189 Participants by Cong Lai, Zhensheng Hu, Jintao Hu, Zhuohang Li, Lin Li, Mimi Liu, Zhikai Wu, Yi Zhou, Cheng Liu, Kewei Xu

“…Five machine learning algorithms were utilized to develop screening models for BCa using the training dataset. …”
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Enhancing early gestational diabetes mellitus prediction with imputation-based machine learning framework: A comparative study on real-world clinical records by Leyao Ma, Lin Yang, Yaxin Wang, Jie Hao, Yini Li, Liangkun Ma, Ziyang Wang, Ye Li, Suhan Zhang, Mingyue Hu, Jiao Li, Yin Sun

“…The classification algorithm primarily accounted for differences in predictive performance. …”
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Metabolomic Profiling Reveals Serum Tryptophan as a Potential Therapeutic Target for Systemic Lupus Erythematosus by Wang K, Zhu R, Xu M, Zhu K, Li J, Li C, Meng D, Chen H, Sun L

“…Eight machine learning algorithms were employed to establish diagnostic models. …”
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Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach by Fetlework Gubena Arage, Zinabu Bekele Tadese, Eliyas Addisu Taye, Tigist Kifle Tsegaw, Tsegasilassie Gebremariam Abate, Eyob Akalewold Alemu

“…These algorithms were employed to predict cervical cancer screening uptake. …”
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Radiomics analysis based on dynamic contrast-enhanced MRI for predicting early recurrence after hepatectomy in hepatocellular carcinoma patients by Kai-Di Wang, Ming-Jing Guan, Zi-Yang Bao, Zhe-Jin Shi, Hai-Hang Tong, Zun-Qiang Xiao, Lei Liang, Jun-Wei Liu, Guo-Liang Shen

“…Patients were randomly allocated to training (n = 140) and validation (n = 60) cohorts. …”
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Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model by Bihua Yao, Xingyu Yu, Liannv Qiu, Er-min Gu, Siyu Mao, Lei Jiang, Jijun Tong, Jianguo Wu

“…The model was built upon 18 routine laboratory parameters, including pleural fluid and serum biomarkers, with multiple machine learning (ML) algorithms evaluated. Light gradient boosting machine (LGBM) emerged as the top-performing model. …”
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Machine Learning and Deep Learning for Diagnosis of Lumbar Spinal Stenosis: Systematic Review and Meta-Analysis by Tianyi Wang, Ruiyuan Chen, Ning Fan, Lei Zang, Shuo Yuan, Peng Du, Qichao Wu, Aobo Wang, Jian Li, Xiaochuan Kong, Wenyi Zhu

“…MethodsThis review was performed under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines using articles extracted from PubMed, Embase databases, and Cochrane Library databases. Studies that evaluated DL or TML algorithms assessment value on diagnosing LSS were included, while those with duplicated or unavailable data were excluded. …”
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Association of microtubule-based processes gene expression with immune microenvironment and its predictive value for drug response in oestrogen receptor-positive breast cancer by Zhenfeng Huang, Minghui Zhang, Nana Zhang, Mengyao Zeng, Yao Qian, Meng Zhu, Xiangyan Meng, Ming Shan, Guoqiang Zhang, Feng Liu

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Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model by Fuyu Guo, Shiwei Sun, Xiaoqian Deng, Yue Wang, Wei Yao, Peng Yue, Shaoduo Wu, Junrong Yan, Junrong Yan, Junrong Yan, Xiaojun Zhang, Xiaojun Zhang, Xiaojun Zhang, Yangang Zhang, Yangang Zhang, Yangang Zhang

“…Various machine learning algorithms were used to construct radiomics, deep learning, and combined models. …”
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Comparison of clinical nasal endoscopy, optical biopsy, and artificial intelligence in early diagnosis and treatment planning in laryngeal cancer: a prospective observational study by Ruifang Hu, Xianping Liu, Yong Zhang, Clement Arthur, Dongguang Qin

“…The AI model was trained on a different pre-annotated dataset, and the images from the study cohort were not used to train the AI model – this methodologically ensures no bias has been introduced into the evaluation. …”
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Continuum topological derivative - A novel application tool for segmentation of CT and MRI images by Viswanath Muthukrishnan, Sandeep Jaipurkar, Nedumaran Damodaran

“…Following this, segmentation of the region of interest was performed using the CTD technique, with comparisons made against Discrete Topological Derivatives (DTD), k-mean clustering and Adaptive Threshold methods. Evaluation of the proposed CTD algorithm's effectiveness in segmentation involved calculating performance metrics such as Jaccard and dice indices to assess spatial overlap of segmented images. …”
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What Is the Predictive Capacity of <i>Sesamum indicum</i> L. Bioparameters Using Machine Learning with Red–Green–Blue (RGB) Images? by Edimir Xavier Leal Ferraz, Alan Cezar Bezerra, Raquele Mendes de Lira, Elizeu Matos da Cruz Filho, Wagner Martins dos Santos, Henrique Fonseca Elias de Oliveira, Josef Augusto Oberdan Souza Silva, Marcos Vinícius da Silva, José Raliuson Inácio da Silva, Jhon Lennon Bezerra da Silva, Antônio Henrique Cardoso do Nascimento, Thieres George Freire da Silva, Ênio Farias de França e Silva

“…Pearson correlation was found to be the most effective data selection method for the algorithm.…”
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Performance of a Retinal Imaging Camera With On-Device Intelligence for Primary Care: Retrospective Study by Matthew Silvestrini, Clarissa Lui, Anil Patwardhan, Ying Chen, Tayyeba Ali, Elie Glik, Honglei Wu, Brian Levinstein, Adrianna Wenz, Nathan Shemonski, Lin Yang, Ian Atkinson, Sam Kavusi

“…In the quality control algorithm evaluation (N=172, K=343 images), we found a positive association (φ ConclusionsOur findings about the performance and usability of this retinal camera system support its deployment as an integrated end-to-end retinal service for primary care. …”
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Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population. by John T Murchison, Gillian Ritchie, David Senyszak, Jeroen H Nijwening, Gerben van Veenendaal, Joris Wakkie, Edwin J R van Beek

“…The deep learning algorithm of the CAD was trained with a lung cancer screening cohort and developed for detection, classification, quantification, and growth of actionable pulmonary nodules on chest CT scans. …”
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Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer by Minghan Jiang, Minghan Jiang, Zeyang Miao, Run Xu, Mengyao Guo, Xuefeng Li, Guanwu Li, Peng Luo, Su Hu, Su Hu

“…Model performance was evaluated by AUC, sensitivity, specificity, accuracy, calibration curves, and decision curve analysis (DCA).ResultsPatients were randomly split into training (n=95) and validation (n=42) cohorts. …”
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