Multimodal ultrasound radiomics containing microflow images for the prediction of central lymph node metastasis in papillary thyroid carcinoma by Jiangyuan Ben, Jiangyuan Ben, Qiying Yv, Pengfei Zhu, Junhao Ren, Pu Zhou, Guifang Chen, Ying He, Ying He

“…The same methods were applied to screen clinical features. Nine ML algorithms were used to construct clinical models, radiomics models and fusion models. …”
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Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images by Mingzhi Zhang, Tsz Kin Ng, Yi Zheng, Guihua Zhang, Jian-Wei Lin, Ji Wang, Jie Ji, Peiwen Xie, Yongqun Xiong, Hanfu Wu, Cui Liu, Huishan Zhu, Jinqu Huang, Leixian Lin

“…The deep learning (DL) performance was compared with the diabetic retinopathy experts.Setting Data were collected from Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Chaozhou People’s Hospital and The Second Affiliated Hospital of Shantou University Medical College from January 2010 to December 2023.Participants 7790 volumes of 7146 eyes from 4254 patients were annotated, of which 6281 images were used as the development set and 1509 images were used as the external validation set, split based on the centres.Main outcomes Accuracy, F1-score, sensitivity, specificity, area under receiver operating characteristic curve (AUROC) and Cohen’s kappa were calculated to evaluate the performance of the DL algorithm.Results In classifying DME with non-DME, our model achieved an AUROCs of 0.990 (95% CI 0.983 to 0.996) and 0.916 (95% CI 0.902 to 0.930) for hold-out testing dataset and external validation dataset, respectively. …”
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Design and optimization of planetary gear train pendulum type sugarcane seeding mechanism based on spatial offset trajectory by Jiaodi Liu, Chaoyuan Luo, Qingli Chen, Jianhao Chen, Jianlong Chen, Yihao Xing

“…Based on the speed requirements of the sugarcane seeds at the critical motion points, a forward kinematics model of this seeding mechanism is established. A multi-objective genetic algorithm combined with the entropy-weight TOPSIS method is used to optimize and screen the installation dimensions of the components of the mechanism so as to keep the motion of the sugarcane seeds stable at the critical positions. …”
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Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis by Hang Chen, Biao Wu, Biao Wu, Kunyu Guan, Liang Chen, Kangjie Chai, Maoji Ying, Dazhi Li, Weicheng Zhao

“…Through further differential analysis and screening using machine learning algorithms, APLNR, PCDH12, PODXL, SLC40A1, TM4SF18, and TNFRSF25 were identified as key diagnostic genes for atherosclerosis. …”
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Construction of mitochondrial signature (MS) for the prognosis of ovarian cancer by Miao Ao, You Wu, Kunyu Wang, Haixia Luo, Wei Mao, Anqi Zhao, Xiaomeng Su, Yan Song, Bin Li

“…After univariate Cox analysis, prognostic genes were carried out for modeling mitochondria signature (MS) based on 101 combinations of 10 machine learning algorithms. …”
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A study on early diagnosis for fracture non-union prediction using deep learning and bone morphometric parameters by Hui Yu, Qiyue Mu, Zhi Wang, Yu Guo, Jing Zhao, Guangpu Wang, Qingsong Wang, Xianghong Meng, Xiaoman Dong, Shuo Wang, Jinglai Sun

“…This study aims to create a fracture micro-CT image dataset, design a deep learning algorithm for fracture segmentation, and develop an early diagnosis model for fracture non-union.MethodsUsing fracture animal models, micro-CT images from 12 rats at various healing stages (days 1, 7, 14, 21, 28, and 35) were analyzed. …”
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Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods by Longlin Jiang, Kexun Li, Simiao Lu, Zhou Hong, Yifang Wang, Qin Xie, Qin He, Sirui Wei, Aoru Zhou, Hong Kang, Xuefeng Leng, Qing Yang, Yan Miao

“…The performance metrics used to evaluate the models including R 2 , root mean square error (RMSE),mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to screen the optimal model. …”
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Data-Driven Battery Remaining Life Prediction Based on ResNet with GA Optimization by Jixiang Zhou, Weijian Huang, Haiyan Dai, Chuang Wang, Yuhua Zhong

“…To this end, this paper proposes a data-driven lithium-ion battery life prediction method based on residual network (ResNet) and genetic algorithm (GA) optimization, which is designed to screen the features of the lithium-ion battery training data in order to effectively reduce the redundant features and improve the prediction performance of the model. …”
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Exploring biomarkers and molecular mechanisms of Type 2 diabetes mellitus promotes colorectal cancer progression based on transcriptomics by Simin Luo, Yuhong Zhu, Zhanli Guo, Chuan Zheng, Xi Fu, Fengming You, Xueke Li

“…The diagnostic performance was assessed by supplementing external datasets to draw ROC curves on the diagnostic model. The diagnostic model was further screened for key genes by prognostic analysis. …”
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The impact of specialised gastroenterology services for pelvic radiation disease (PRD): Results from the prospective multi-centre EAGLE study. by John N Staffurth, Stephanie Sivell, Elin Baddeley, Sam Ahmedzai, H Jervoise Andreyev, Susan Campbell, Damian J J Farnell, Catherine Ferguson, John Green, Ann Muls, Raymond O'Shea, Sara Pickett, Lesley Smith, Sophia Taylor, Annmarie Nelson

“…All men completed a validated screening tool for late bowel effects (ALERT-B) and the Gastrointestinal Symptom Rating Score (GSRS); men with a positive score on ALERT-B were offered management following a peer reviewed algorithm for pelvic radiation disease (PRD). …”
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A machine learning approach to predict positive coronary artery calcium scores in individuals with diabetes: a cross-sectional analysis of ELSA-Brasil baseline data by J.L. Amorim, I.M. Bensenor, A.P. Alencar, A.C. Pereira, A.C. Goulart, P.A. Lotufo, I.S. Santos

“…We analyzed 25 sociodemographic, medical history, symptom-related, and laboratory variables from 585 participants from the São Paulo investigation center with CACS data and no overt cardiovascular disease at baseline. We used six ML algorithms to build models to identify individuals with positive CACS. …”
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GB-SAR Engineering Interference Suppression Method Integrating Amplitude-Phase Feature Analysis and Robust Regression by Wenting Zhang, Tao Lai, Yuanhui Mo, Haifeng Huang, Qingsong Wang, Zhihua Zhou

“…Subsequently, a two-stage suppression model based on robust estimation theory is developed to effectively suppress interference. …”
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Role of arachidonic acid metabolism in osteosarcoma prognosis by integrating WGCNA and bioinformatics analysis by Yaling Wang, Peichun HSU, Haiyan Hu, Feng Lin, Xiaokang Wei

“…An AA metabolism predictive model of the five AAMRGs were established by Cox regression and the LASSO algorithm. …”
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A comparison of tools used for tuberculosis diagnosis in resource-limited settings: a case study at Mubende referral hospital, Uganda. by Adrian Muwonge, Sydney Malama, Barend M de C Bronsvoort, Demelash Biffa, Willy Ssengooba, Eystein Skjerve

“…The three-predictor screening algorithm with and without DZM classified 50% and 33% of the true cases respectively, while the adjusted algorithm with DZM classified 78% of the true cases.…”
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End-to-end deep fusion of hyperspectral imaging and computer vision techniques for rapid detection of wheat seed quality by Tingting Zhang, Jing Li, Jinpeng Tong, Yihu Song, Li Wang, Renye Wu, Xuan Wei, Yuanyuan Song, Rensen Zeng

“…Applying this model to seed lot screening increased the proportion of high-quality seeds from 47.7 % to 93.4 %. …”
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An interpretable disruption predictor on EAST using improved XGBoost and SHAP by D.M. Liu, X.L. Zhu, Y.S. Jiang, S. Wang, S.B. Shu, B. Shen, B.H. Guo, L.C. Liu

“…Based on the physical characteristics of the disruption, 2094 disruption shots and 4858 non-disruption shots from 2022 to 2024 were screened as training shots, and then the disruption prediction model was trained using the eXtreme Gradient Boosting (XGBoost) algorithm from training samples consisting of 16 diagnostic signals, such as plasma current, density, and radiation. …”
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Diagnosing facial synkinesis using artificial intelligence to advance facial palsy care by Leonard Knoedler, Christian Festbaum, Jillian Dean, Helena Baecher, Grégoire de Lambertye, Maximilian Maul, Thomas Schaschinger, Tobias Niederegger, Alexandra Scheiflinger, Michael Alfertshofer, Khalil Sherwani, Claudius Steffen, Max Heiland, Steffen Koerdt, Samuel Knoedler, Andreas Kehrer

“…This study aimed to develop a cost-effective, rapid, and accurate artificial intelligence (AI)-based algorithm to screen FP patients for facial synkinesis. …”
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The value of habitat analysis based on 18F-PSMA-1007 PET/CT images for prostate cancer risk grading by Yang Wang, Hongyue Zhao, Zhehao Lyu, Linhan Zhang, Wei Han, Zeyu Wang, Jiafu Wang, Xinyue Zhang, Shibo Guo, Peng Fu, Changjiu Zhao

“…Independent risk factors were screened and a combined model was constructed to predict GS grade by univariate logistic regression followed by multivariate logistic regression of habitat (1–4) and clinical factors (SUVmax, tPSA, fPSA/tPSA, age). …”
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Exploration of the Prognostic Markers of Multiple Myeloma Based on Cuproptosis‐Related Genes by Xiao‐Han Gao, Jun Yuan, Xiao‐Xia Zhang, Rui‐Cang Wang, Jie Yang, Yan Li, Jie Li

“…Additionally, key module genes were identified through weighted gene co‐expression network analysis. A univariate Cox algorithm and multivariate Cox analysis were employed to obtain biomarkers of MM and build a prognostic model before conducting independent prognostic analysis. …”
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Analyzing adjustment and verification errors in electric metering devices for smart power systems considering multiple environmental factors by Chuanliang He, Xin Xia, Bo Zhang, Wei Kang, Jinxia Zhang, Haipeng Chen

“…Then, an error adjustment model based on gated recurrent unit-attention is constructed, and the particle swarm optimization algorithm is adopted for the purpose of optimizing hyperparameters. …”
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