Breast Cancer Screening Using a Modified Inertial Projective Algorithms for Split Feasibility Problems by Pennipat Nabheerong, Warissara Kiththiworaphongkich, Watcharaporn Cholamjiak

“…To detect breast cancer in mammography screening practice, we modify the inertial relaxed CQ algorithm with Mann’s iteration for solving split feasibility problems in real Hilbert spaces to apply in an extreme learning machine as an optimizer. …”
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Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus images by M. Shanmuga Eswari, S. Balamurali, Lakshmana Kumar Ramasamy

“…Objective We developed an optimized decision support system for retinal fundus image-based glaucoma screening. Methods We combined computer vision algorithms with a convolutional network for fundus images and applied a faster region-based convolutional neural network (FRCNN) and artificial algae algorithm with support vector machine (AAASVM) classifiers. …”
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An open dataset and machine learning algorithms for Niacin Skin-Flushing Response based screening of psychiatric disorders by Xuening Lyu, Rimsa Goperma, Dandan Wang, Chunling Wan, Liang Zhao

“…The core of our methodology involves a novel algorithm featuring an Efficient-Unet based Deep Learning model for the precise segmentation of NSR areas. …”
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Sure Independence Screening for Ultrahigh-Dimensional Additive Model with Multivariate Response by Yongshuai Chen, Baosheng Liang

“…This paper investigated an ultrahigh-dimensional feature screening approach for additive models with multivariate responses. …”
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Deep learning based screening model for hip diseases on plain radiographs. by Jung-Wee Park, Seung Min Ryu, Hong-Seok Kim, Young-Kyun Lee, Jeong Joon Yoo

“…This study aimed to develop and validate a deep learning-based screening model for distinguishing normal hips from severe hip diseases on plain radiographs.…”
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Influencing factors of cross screening rate and its intelligent prediction model by Lala ZHAO, Feng XU, Chenlong DUAN, Chenhao GUO, Wei WANG, Haishen JIANG, Jinpeng QIAO

“…Based on linear regression (LR), support vector machine (SVM), decision tree (DT) and random forest (RF) algorithms, four intelligent prediction models of cross screening rate were established. …”
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Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies. by Cox Lwaka Tamba, Yuan-Li Ni, Yuan-Ming Zhang

“…This method is referred to as ISIS EM-BLASSO algorithm. Monte Carlo simulation studies validated the new method, which has the highest empirical power in QTN detection and the highest accuracy in QTN effect estimation, and it is the fastest, as compared with efficient mixed-model association (EMMA), smoothly clipped absolute deviation (SCAD), fixed and random model circulating probability unification (FarmCPU), and multi-locus random-SNP-effect mixed linear model (mrMLM). …”
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A novel lightweight multi-scale feature fusion segmentation algorithm for real-time cervical lesion screening by Jiahui Yang, Ying Zhang, Wenlong Fan, Jie Wang, Xinhe Zhang, Chunhui Liu, Shuang Liu, Linyan Xue

“…Therefore, a lightweight algorithm segmentation for cervical lesion real-time screening system is urgently needed. …”
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XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis by Sudi Suryadi, Masrizal

“…This study is situated at the intersection of clinical oncology and computational intelligence, exploring the potential of gradient-boosting algorithms to overcome the limitations of conventional screening methodologies. …”
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Screening benzimidazole derivatives for atypical antipsychotic activity by K. Yu. Kalitin, O. Yu. Mukha, V. B. Voynov

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Cost-effectiveness analysis of MASLD screening using FIB-4 based two-step algorithm in the medical check-up by Mimi Kim, Huiyul Park, Eileen L. Yoon, Ramsey Cheung, Donghee Kim, Hye-Lin Kim, Dae Won Jun

“…We constructed a hybrid model of the decision tree model and Markov model to compare expected costs and quality-adjusted life-years (QALYs) between ‘screening’ and ‘no screening’ groups from healthcare system perspectives. …”
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Performance of machine learning-based models to screen obstructive sleep apnea in pregnancy by Jingyu Wang, Wenhan Xiao, Haoyang Hong, Chi Zhang, Min Yu, Liyue Xu, Jun Wei, Jingjing Yang, Yanan Liu, Huijie Yi, Linyan Zhang, Rui Bai, Bing Zhou, Long Zhao, Xueli Zhang, Xiaozhi Wang, Xiaosong Dong, Guoli Liu, Shenda Hong

“…Abstract The purpose of this study is to improve the performance of existing OSA screening tools for pregnant women with machine learning algorithms. …”
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Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI by Sihwan Kim, Changmin Park, Gwanghyeon Jeon, Seohee Kim, Jong Hyo Kim

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Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm by Yushuai Yuan, Li Yang, Rui Gao, Cheng Chen, Min Li, Jun Tang, Xiaoyi Lv, Ziwei Yan

“…The samples were input into the SVM after division by the Kennard–Stone (KS) algorithm. Compared with other models, the SVM optimized by a grid search (GS) algorithm performed the best. …”
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Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera by Ashish Sharma, Taraprasad Das, Bryan Ong, Florian Mickael Savoy, Divya Parthasarathy Rao, Jun Kai Toh, Anand Sivaraman

“…The model was designed to indicate the presence of referable AMD (intermediate and advanced AMD). …”
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To Determine the Risk-Based Screening Interval for Diabetic Retinopathy: Development and Validation of Risk Algorithm from a Retrospective Cohort Study by Jinxiao Lian, Ching So, Sarah Morag McGhee, Thuan-quoc Thach, Cindy Lo Kuen Lam, Colman Siu Cheung Fung, Alfred Siu Kei Kwong, Jonathan Cheuk Hung Chan

“…Methods The retrospective cohort data from 117,418 subjects who received systematic DR screening in Hong Kong between 2010 and 2016 were included to develop and validate the risk algorithm using a parametric survival model. …”
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High-content chemical and RNAi screens for suppressors of neurotoxicity in a Huntington's disease model. by Joost Schulte, Katharine J Sepp, Chaohong Wu, Pengyu Hong, J Troy Littleton

“…By tracking the subcellular distribution of mRFP-tagged pathogenic Huntingtin and assaying neurite branch morphology via live-imaging, we identified suppressors that could reduce Huntingtin aggregation and/or prevent the formation of dystrophic neurites. The custom algorithms we used to quantify neurite morphologies in complex cultures provide a useful tool for future high-content screening approaches focused on neurodegenerative disease models. …”
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Development and validation of machine learning models for MASLD: based on multiple potential screening indicators by Hao Chen, Jingjing Zhang, Xueqin Chen, Ling Luo, Wenjiao Dong, Yongjie Wang, Jiyu Zhou, Canjin Chen, Wenhao Wang, Wenbin Zhang, Zhiyi Zhang, Yongguang Cai, Danli Kong, Yuanlin Ding

“…This study aimed to utilize multifaceted indicators to construct MASLD risk prediction machine learning models and explore the core factors within these models.MethodsMASLD risk prediction models were constructed based on seven machine learning algorithms using all variables, insulin-related variables, demographic characteristics variables, and other indicators, respectively. …”
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Key gene screening and diagnostic model establishment for acute type a aortic dissection by Yue Pan, Zhiming Yu, Xiaoyu Qian, Xuesong Zhang, Qun Xue, Weizhang Xiao

“…The multi-omics diagnostic model provides a novel tool for early screening, potentially reducing mortality through timely intervention. …”
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Genomic and algorithm-based predictive risk assessment models for benzene exposure by Minyun Jiang, Minyun Jiang, Na Cai, Na Cai, Juan Hu, Lei Han, Lei Han, Fanwei Xu, Baoli Zhu, Baoli Zhu, Baoli Zhu, Baoli Zhu, Boshen Wang

“…AimIn this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes associated with occupational benzene exposure and to construct genomic and algorithm-based predictive risk assessment models.Subject and methodsWe sourced GSE9569 and GSE21862 microarray data from the Gene Expression Omnibus. …”
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