Diagnostic Value of F-FDG PET/CT Radiomics in Lymphoma: A Systematic Review and Meta-Analysis by Chaoying Liu MD, Jun Zhao PhD, Heng Zhang PhD, Xinye Ni PhD

“…Conclusion The results supported that 18 F-FDG PET/CT radiomics has high diagnostic value for lymphoma. However, there is high heterogeneity among different studies. …”
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Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis by Xingren Liu, Junmei Song, Shujin Guo, Yi Liao, Jun Zou, Liqing Yang, Caiyu Jiang

“…Weighted gene co-expression network analysis (WGCNA) was performed on the identified clusters to determine core modules and hub genes associated with IPF. Machine learning algorithms were applied to these hub genes to construct diagnostic and prognostic models, which were validated across multiple datasets. …”
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Bioinformatics‑Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis by Yang M, Zhang X, Zhou C, Du Y, Zhou M, Zhang W

“…Least Absolute Shrinkage and Selection Operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen hub genes. Immune cell infiltration was evaluated using CIBERSORT. …”
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IT Parkinson’s Disease Diagnostics Based on the Freezing of Gait Analysis Using Long Short Term Memory Neural Network by U. A. Vishniakou, Xia YiWei

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Research on bearing fault diagnosis based on improved northern goshawk algorithm optimizing SVM by WU Xiaojun, LI Quwei

“…The results show that the improved algorithm can effectively and accurately achieve different fault classifications, with an accuracy rate of 99.39%, verifying the effectiveness and feasibility of this method.…”
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Prescription Recommendation Algorithm Based on Herbal Property-Driven Compatibility Mechanism Semantic Modeling by Geng Xueru, Zhang Jiantong, Luo Tao, Hou Jianchen, Tao Xiaohua

“…Finally, an attention mechanism is employed to fuse information from symptom interaction graphs, symptom-herb interaction graphs, and herb interaction graphs, distinguishing the influence of different dimensions of TCM semantic information. Experimental results, compared with existing formula generation algorithms, demonstrate that HPDCM achieves higher accuracy and is more in line with the TCM diagnostic and therapeutic principles of syndrome differentiation and treatment.…”
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Diagnosing schizophrenia with routine blood tests: a comparative analysis of machine learning algorithms by Yavuz Selim Ogur, Ayse Erdogan Kaya, Nur Banu Ogur, Beyza Erdogan Akturk

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A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques by Yuanyuan Wang, Fangfang Wang, Zixin Qin, Yongcheng Fu, Jingyue Wang, Shangkun Li, Da Zhang

“…However, the high degree of similarity among neuroblastoma subtypes complicates the diagnostic process. In response to these challenges, this study presents a modified version of the You Only Look Once (YOLO) algorithm, called YOLOv8-IE. …”
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Exploring the role of alternative lengthening of telomere-related genes in diagnostic modeling for non-alcoholic fatty liver disease by Nan Zhu, Xiaoliang Wang, Huiting Zhu, Yue Zheng

“…This study employed a support vector machine algorithm and least absolute shrinkage and selection operator regression analysis to identify key genes for constructing a diagnostic model. …”
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Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection by Ghufran Basim Alghanimi, Hadeel Aljobouri, Khaleel Akeash Alshimmari, Rasha Massoud

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Impact of a deep-learning image reconstruction algorithm on image quality and detection of solid lung lesions by Joël Greffier, Maxime Pastor, Quentin Durand, Renaud Sales, Chris Serrand, Jean-Paul Beregi, Djamel Dabli, Julien Frandon

“…Statistical analyses was performed to compare outcomes obtained with the different algorithms. Results: Thirty patients with a mean age of 70.0 ± 9.0 years (17 men) were included. …”
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Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss by Rui Zhang, Yuanbing Guo, Xiaonan Zhai, Juan Wang, Xiaoyan Hao, Liu Yang, Lei Zhou, Jiawei Gao, Jiayun Liu

“…Three methods were used for screening 100+ clinical indicators, and five machine learning algorithms were used to develop and evaluate diagnostic models based on the most relevant indicators.ResultsMultivariate analysis revealed significant differences in several factors between the patients and the control group. 25-hydroxy vitamin D3 (25OHVD3) was the factor exhibiting the most prominent difference, and most patients presented deficiency in the levels of this vitamin. 25OHVD3 is associated with blood lipids, hormones, thyroid function, human papillomavirus infection, hepatitis B infection, sedimentation rate, renal function, coagulation function, and amino acids in patients with infertility. …”
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A machine learning based algorithm accurately stages liver disease by quantification of arteries by Zhengxin Li, Xin Sun, Zhimin Zhao, Qiang Yang, Yayun Ren, Xiao Teng, Dean C. S. Tai, Ian R. Wanless, Jörn M. Schattenberg, Chenghai Liu

“…AD and CPA performed similar diagnostic efficacy in liver cirrhosis (P > 0.05). Using the qVessel algorithm, we discovered a significant correlation between AD, CPA and METAVIR stages in all three etiologies. …”
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Modern aspects of diagnostics and treatment of <i>H. pylori</i> infection from standpoint of stomach cancer prophylaxis by A. A. Sheptulin, T. L. Lapina

“…Comparative estimation of efficacy of different modes of eradication therapy is given. According to conclusion of the majority of publications and data of meta-analyses, nowadays both bismuth-based and bismuth-free quadrotherapy are considered as the most effective treatment algorithms.Conclusion. …”
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Prediction of Optimum Operating Parameters to Enhance the Performance of PEMFC Using Machine Learning Algorithms by Arunadevi M, Karthikeyan B, Anirudh Shrihari, Saravanan S, Sundararaju K, R Palanisamy, Mohamed Awad, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Abdulrahman Al Ayidh, Hany S. Hussein, Mahmoud M. Hussein, Ahmed I. Omar

“…With a high degree of accuracy, machine learning algorithms (MLAs) can be applied to solve nonlinear problems in FCs, including performance prediction, service life prediction, and fault diagnostics. …”
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CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization. by Gowtham A Rao, Azza Shoaibi, Rupa Makadia, Jill Hardin, Joel Swerdel, James Weaver, Erica A Voss, Mitchell M Conover, Stephen Fortin, Anthony G Sena, Chris Knoll, Nigel Hughes, James P Gilbert, Clair Blacketer, Alan Andryc, Frank DeFalco, Anthony Molinaro, Jenna Reps, Martijn J Schuemie, Patrick B Ryan

“…<h4>Objective</h4>This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics.…”
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Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases by Miad Boodaghidizaji, Thaisa Jungles, Tingting Chen, Bin Zhang, Tianming Yao, Alan Landay, Ali Keshavarzian, Bruce Hamaker, Arezoo Ardekani

“…We demonstrated that machine learning algorithms can classify diseases with accuracy as high as 95%. …”
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Unified total body CT image with multiple organ specific windowings: validating improved diagnostic accuracy and speed in trauma cases by Naoki Okada, Shusuke Inoue, Chang Liu, Sho Mitarai, Shinichi Nakagawa, Yohsuke Matsuzawa, Satoshi Fujimi, Goshiro Yamamoto, Tomohiro Kuroda

“…The Uni-CT algorithm improved the diagnostic speed of total-body CT for trauma, maintaining accuracy comparable to that of conventional methods.…”
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Fine-Tuned Machine Learning Classifiers for Diagnosing Parkinson’s Disease Using Vocal Characteristics: A Comparative Analysis by Mehmet Meral, Ferdi Ozbilgin, Fatih Durmus

“…This study seeks to assess the effectiveness of machine learning algorithms optimized to classify PD based on vocal characteristics to serve as a non-invasive and easily accessible diagnostic tool. …”
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Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction by Zhengbiao Hu, Shuangshan Bu, Kai Wang, Qianqian Cao, Huanhuan Zheng, Jie Yang, Shanshan Chen, Yuemeng Wu, Wenkai Ren, Chenlei He

“…In this study, multiple machine learning algorithms were used to develop a non-invasive and improved model for the diagnosis of IgAN. …”
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