Automated Detection and Evaluation of Swallowing Using a Combined EMG/Bioimpedance Measurement System by Corinna Schultheiss, Thomas Schauer, Holger Nahrstaedt, Rainer O. Seidl

“…Our two-stage algorithm was able to reliably detect swallows (sensitivity = 96.1%, specificity = 97.1%) on the basis of these differences. …”
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Recent Advancements in Neuroimaging‐Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e‐Health: A Systematic Review by Zia‐Ur‐Rehman, Mohd Khalid Awang, Ghulam Ali, Muhammad Faheem

“…This comprehensive review intends to examine current developments in deep learning (DL) approaches with neuroimaging for AD diagnosis, where popular imaging types, reviews well‐known online accessible data sets, and describes different algorithms used in DL for the correct initial evaluation of AD are presented. …”
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LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study by Hengrui Liang, Runchen Wang, Ran Cheng, Zhiming Ye, Na Zhao, Xiaohong Zhao, Ying Huang, Zhanpeng Jiang, Wangzhong Li, Jianqi Zheng, Hongsheng Deng, Yu Jiang, Yuechun Lin, Yun Yan, Lei Song, Jie Li, Xin Xu, Wenhua Liang, Jun Liu, Jianxing He

“…After integrating clinical factors, a significant increase diagnostic accuracy was observed with AUC of .91 (.85‒.98). …”
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An Approach to Endophthalmitis by Aditya Sharma, Siddharth Madan, Nitika Beri

“…The algorithm and protocol for management of endophthalmitis has been explained in detail. …”
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Noninvasive Quantification of Nausea Severity in Pediatric Functional Nausea by Nicole D. Muszynski, Suseela Somarajan, Jordan D. Busing, Alexandra C. Russell, Sari A. Acra, Leonard A. Bradshaw

“…Background and Aims: Functional nausea is an idiopathic gastrointestinal disorder that is common in children, carries a substantial health-care cost, and lacks objective diagnostic testing, relying primarily on symptomatology with an otherwise normal diagnostic workup. …”
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INCIDENCE OF PSORIATIC ARTHRITIS IN RUSSIA: TRENDS AT THE PRESENT STAGE AND PROSPECTS by O. S. Mishina, T. V. Korotaeva, V. I. Starodubov, E. L. Nasonov

“…The alarming fact is that one-third of psoriatic patients experience joint pain and only 8% are diagnosed as having PsA, which also points to the inadequate diagnosis of this disease.The official statistical data on the incidence of PsA in the Russian Federation greatly differ from the results of international epidemiological surveys, which may be associated both with poor record keeping and diagnostic problems. …”
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Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer by Nektarios Georgiou, Pavlos Kolias, Ioanna Chouvarda

“…Additionally, this study investigated the use of feature extraction techniques to derive meaningful data from the images, contributing to a more nuanced understanding of cancerous tissues, comparing the performance of different image classification algorithms in the context of colon cancer image analysis. …”
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GLN2 as a key biomarker and therapeutic target: evidence from a comprehensive pan-cancer study using molecular, functional, and bioinformatic analyses by Shuang Gao, Lei Zhang, Guoping Sun

“…However, its expression and function in different types of tumors are not well understood. …”
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Chronic Hepatitis С in Siberian Federal District: Analysis of a Case Rate and Evolution of Genotypes of the Originator by E. D. Savilov, S. I. Malov, I. V. Malov, O. B. Ogarkov, V. A. Astafev, N. N. Chemezova, I. A. Miroshnichenko

“…For integrated assessment of an epidemiological situation indicators of a case rate of the chronic hepatitis C and statistical criteria are used: a standard deviation and rate of a gain of a case rate and also an algorithm of calculation of an integrated indicator in the form of presentation coefficient. …”
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Application of decision tree model in diagnosis of mycoplasma pneumoniae pneumonia with plastic bronchitis by Lin Li, Dong Wang, Rongrong Yang, Xing Liao, Ling Wu

“…Using R language, 70% of the data from each group of patients was randomly selected for training the model using decision tree algorithm analysis, thus generating a clinical diagnostic decision tree for Mycoplasma pneumoniae (MP) combined with PB. …”
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A Multitask Network for the Diagnosis of Autoimmune Gastritis by Yuqi Cao, Yining Zhao, Xinao Jin, Jiayuan Zhang, Gangzhi Zhang, Pingjie Huang, Guangxin Zhang, Yuehua Han

“…First, we employ the Scale-Invariant Feature Transform (SIFT) algorithm for image registration, generating an image similarity matrix. …”
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Sex-Specific Ensemble Models for Type 2 Diabetes Classification in the Mexican Population by Mendoza-Mendoza MM, Acosta-Jiménez S, Galván-Tejada CE, Maeda-Gutiérrez V, Celaya-Padilla JM, Galván-Tejada JI, Cruz M

“…Accurate early diagnosis remains a challenge, especially when accounting for biological sex-based differences.Purpose: This study aims to enhance the classification of T2D in the Mexican population by applying sex-specific ensemble models combined with genetic algorithm-based feature selection.Materials and Methods: A dataset of 1787 Mexican patients (895 females, 892 males) is analyzed. …”
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Clinical and morphological approaches to the differential diagnosis of diphtheric colitis by V. A. Tsinserling, E. I. Belinskaya, V. V. Svistunov

“…The algorithm for optimizing of differential diagnostics of inflammatory bowel diseases with recommendations for qualitative morphological examination has been suggested.…”
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Identification of signature genes and subtypes for heart failure diagnosis based on machine learning by Yanlong Zhang, Yanming Fan, Fei Cheng, Dan Chen, Hualong Zhang

“…Consequently, identifying specific genes for HF at the transcriptomic level may enhance early detection and allow for more targeted therapies for these individuals.MethodsHF datasets were acquired from the Gene Expression Omnibus (GEO) database (GSE57338), and through the application of bioinformatics and machine-learning algorithms. We identified four candidate genes (FCN3, MNS1, SMOC2, and FREM1) that may serve as potential diagnostics for HF. …”
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Industrial multi-machine data aggregation, AI-ready data preparation, and machine learning for virtual metrology in semiconductor wafer and slider production by Feiyang Ou, Julius Suherman, Chao Zhang, Henrik Wang, Sthitie Bom, James F. Davis, Panagiotis D. Christofides

“…In a previous paper, we considered how to aggregate data from different etch tools in the same processes at different factories within Seagate Technology and proposed a method for data aggregation and demonstrated its value (Ou et al., 2024). …”
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The value of a radiomics model in predicting ovarian malignancy: a retrospective multi-center comparison with O-RADS and radiologists by Junjie Jin, Xijia Deng, Ling Long, Meiling Liu, Meimei Cao, Hao Gong, Huan Liu, Xiaosong Lan, Lili Liu, Jiuquan Zhang

“…Features were selected using minimum redundancy, maximum relevance, and the least absolute shrinkage and selection operator algorithm. Diagnostic performance of the radiomics model, O-RADS, and independent assessments by junior and senior radiologists was evaluated via the area under the receiver operating characteristic curve (AUC) and compared using DeLong’s test. …”
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Clear Cell Acanthoma/Pseudolymphoma: Clinical­ Dermatoscopic and Histological Correlation by Shulaia T., Kiladze N., Miriamidze A.

“…Differential diagnosis of such tumors is still an urgent problem, and a variety of diagnostic methods does not solve the problem. Today, the most affordable method of optical diagnostics of the skin is dermatoscopy, the non­invasiveness and simplicity of which made it indispensable in modern practice. …”
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Development of a method for differential diagnosis of iron deficiency anemia and anemia of chronic disease based on demographic data and routine laboratory tests using machine lear... by N. V. Varekha, N. I. Stuklov, K. V. Gordienko, R. R. Gimadiev, O. B. Shchegolev, S. N. Kislaya, E. V. Gubina, A. A. Gurkina

“…The obtained classification model has high diagnostic accuracy for different clinical groups according to the SF level (AuC ROC was 0.91; 0.79; 0.84; 0.90 and 0.96; 0.76; 0.71; 0.82 for patients with reduced hemoglobin levels in women (< 120 g / L) and men (< 130 g / L) in groups I, II, III, Iv, respectively).   …”
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Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease by I-Wen Wu, Yu-Chieh Liao, Tsung-Hsien Tsai, Chieh-Hua Lin, Zhao-Qing Shen, Yun-Hsuan Chan, Chih-Wei Tu, Yi-Ju Chou, Chi-Jen Lo, Chi-Hsiao Yeh, Chun-Yu Chen, Heng-Chih Pan, Heng-Jung Hsu, Chin-Chan Lee, Mei-Ling Cheng, Wayne Huey-Herng Sheu, Chi-Chun Lai, Huey-Kang Sytwu, Ting-Fen Tsai

“…Firstly, new types of microbiota biomarkers have been created using a machine-learning (ML) method, namely relative abundance of a microbe, presence or absence of a microbe, and the hierarchy ratio between two different taxonomies. Four different panels of features were selected to be analyzed: (i) DM vs. …”
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Detection of breast cancer using machine learning and explainable artificial intelligence by Tharunya Arravalli, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, D. Cenitta, Rajagopala Chadaga, K. S. Swathi

“…The research emphasized the results obtained by explainers such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), ELI5 (Explain Like I’m Five), Anchor and QLattice (Quantum Lattice) to decipher the findings. Interpretable algorithms can be applied in the medical sector to assist practitioners in predicting breast cancer, reducing diagnostic errors, and improving clinical decision-making.…”
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