Urine flow cytometry in older adults urinary tract infection diagnosis: is it time to reevaluate thresholds for men and women? by Adrien Biguenet, Elisa Champy, Marine Gilis, Houssein Gbaguidi-Haore, Isabelle Patry, Kévin Bouiller, Xavier Bertrand

“…Urine dipsticks effectively excluded UTI in men (LR- < 0.1), but were less reliable in women (LR- = 0.129). Gender-specific diagnostic algorithms were suggested. Conclusions Urine flow cytometry provides valuable diagnostic thresholds for bacteriuria and leukocyturia and helps to exclude UTI before culture results. …”
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Radiomic Feature Extraction Based on Computed Tomography for Stroke Patients by Alamgir Hossain, Jolly Sultana, Shahed Hossain, Hafizur Rahman

“…Conclusion: In summary, stroke imaging analysis benefits greatly from the deployment of complex image processing techniques, reliable feature extraction methods, and cutting-edge segmentation algorithms. Accurate stroke lesion identification and stratification are made possible by the thorough assessment of diagnostic parameters such as ROC curves and the application of visualization tools such as heatmaps.…”
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Optimized Time-domain Feature Extraction for Early Onset Diagnosis of Parkinson Disease From EEG Signals by Delshad Ghavami, Moein Radman, Ali Chaibakhsh

“…Brain signal analysis, a non-invasive and reliable technique, provides an alternative or complementary method to traditional diagnostic approaches. Objectives: This study aims to develop a diagnostic method for PD by combining signal processing techniques with machine learning (ML) algorithms. …”
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MRI quantified enlarged perivascular space volumes as imaging biomarkers correlating with severity of anxiety depression in young adults with long-time mobile phone use by Li Li, Yalan Wu, Jiaojiao Wu, Bin Li, Rui Hua, Feng Shi, Lizhou Chen, Yeke Wu

“…For classifying depression status, eight features were selected to construct a K nearest neighbors model with an AUC value of 0.931 (95%CI 0.814-1.000) in the testing dataset.DiscussionThe utilization of MRI-quantified EPVS metrics combined with machine-learning algorithms presents a promising method for evaluating severity of anxiety and depression symptoms in patients with LTMPU, which might introduce a non-invasive, objective, and quantitative approach to enhance diagnostic efficiency and guide personalized treatment strategies.…”
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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

“…Based on key operating parameters like voltage, current, and speed, this article describes how machine learning (ML) algorithms like Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Support Vector Machines (SVM), and Extreme Gradient Boosting with Feature Interaction (XGBoost + FIS) are used to detect different motor faults. …”
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Deep learning-based detection and classification of acute lymphoblastic leukemia with explainable AI techniques by Debendra Muduli, Sourav Parija, Suhani Kumari, Asmaul Hassan, Harendra S. Jangwan, Abu Taha Zamani, Sk. Mohammed Gouse, Banshidhar Majhi, Nikhat Parveen

“…A comprehensive evaluation highlights the effectiveness of deep learning in distinguishing different types of ALL, demonstrating its potential as a reliable diagnostic tool in medical imaging. …”
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Genital ulcer disease: current challenges and perspectives by L. O. Kovtun

“…To analyze the role of etiological factors of genital ulcer disease based on data from professional literature and to develop proposals for improving diagnostic algorithms, as well as a syndromic approach to treatment and prevention, in particular, regarding current trends in world medical practice. …”
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Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network by Xingyu Duan, Xiaojun Ma, Mengqi Zhu, Linan Wang, Dingqi You, Lili Deng, Ningkui Niu

“…After automatic segmentation of the back contour, a back asymmetry index was calculated via computer vision algorithms to classify scoliosis into different severities. …”
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Randomised controlled trial to evaluate the effectiveness of using the RD-1-based C-Tb skin test as a replacement for blood-based interferon-γ release assay for detection of, and i... by Marc Lipman, Ibrahim Abubakar, Chris Griffiths, Penny Whiting, Robert Horne, Alex Ghanouni, Heinke Kunst, Mike Mandelbaum, Molebogeng X Rangaka, Karen Sanders, Peter J White, Angela Crook, Dominik Zenner, Trinh Duong, Vanessa Hack, Julian Surey, Yohhei Hamada, Henry Bern, Joanna Calvert, Marie Francis, Amy Louise Clarke, Ellen Owen-Powell, Helen L Booth

“…Economic analysis will estimate cost-effectiveness of changes to the diagnostic algorithm for LTBI. The protocol was developed with Patient and Public Involvement (PPI), which will continue throughout the trial.Ethics and dissemination Ethics approval has been obtained from The NHS Health Research Authority (269485). …”
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Analysis on pulse features of coronary heart disease patients with or without a history of ischemic stroke by Li Xin, Li Wei, Ng Man-In, Parry Natalie Ann, Li Siqi, Li Rui, Guo Rui

“…The developed pulse-based recognition model holds the potential in distinguishing between these three groups, offering a novel diagnostic reference for clinical practice.…”
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Artificial Intelligence in Chest Radiography—A Comparative Review of Human and Veterinary Medicine by Andrea Rubini, Roberto Di Via, Vito Paolo Pastore, Francesca Del Signore, Martina Rosto, Andrea De Bonis, Francesca Odone, Massimo Vignoli

“…The integration of artificial intelligence (AI) into chest radiography (CXR) has greatly impacted both human and veterinary medicine, enhancing diagnostic speed, accuracy, and efficiency. In human medicine, AI has been extensively studied, improving the identification of thoracic abnormalities, diagnostic precision in emergencies, and the classification of complex conditions such as tuberculosis, pneumonia, and COVID-19. …”
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A Complementary Dataset of Scalp EEG Recordings Featuring Participants with Alzheimer’s Disease, Frontotemporal Dementia, and Healthy Controls, Obtained from Photostimulation EEG by Aimilia Ntetska, Andreas Miltiadous, Markos G. Tsipouras, Katerina D. Tzimourta, Theodora Afrantou, Panagiotis Ioannidis, Dimitrios G. Tsalikakis, Konstantinos Sakkas, Emmanouil D. Oikonomou, Nikolaos Grigoriadis, Pantelis Angelidis, Nikolaos Giannakeas, Alexandros T. Tzallas

“…This new dataset enables the study of brain responses to visual stimulation across different cognitive states and supports the development and validation of automated classification algorithms for dementia detection. …”
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Identification of key ferroptosis-related genes and therapeutic target in nasopharyngeal carcinoma by Yuanyuan Gu, Yaozhuang Zhou, Chunhua Xie, Guangyao He, Guangyao He, Maosheng Zhang

“…WGCNA revealed 34 disease-related genes, and four hub genes (TBK1, KIF20A, SLC16A1, QSOX1) were selected, showing high diagnostic efficacy. GSEA/GSVA highlighted pathway differences between high/low expression groups. …”
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"Proof-of-concept" evaluation of an automated sputum smear microscopy system for tuberculosis diagnosis. by James J Lewis, Violet N Chihota, Minty van der Meulen, P Bernard Fourie, Katherine L Fielding, Alison D Grant, Susan E Dorman, Gavin J Churchyard

“…<h4>Background</h4>"TBDx" is an innovative smear microscopy system that automatically loads slides onto a microscope, focuses and digitally captures images and then classifies smears as positive or negative using computerised algorithms.<h4>Objectives</h4>To determine the diagnostic accuracy of TBDx, using culture as the gold standard, and compare this to a microscopist's diagnostic performance.…”
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The Relationship Between Stiff Knee Gait Runner’s Dystonia and Musculoskeletal Knee Pathology: A Case Series by Jared A. Stowers, Derek S. Day, Steven Jow, Sarah Heins, Euan Forrest, Yonathan M. Assefa, Paige M. Lind, Afreen Mushtaheed, Frances T. Sheehan, Katharine E. Alter

“…Treatment of RD is challenging and often confounded by significant diagnostic delays due to overlapping symptomatology with other conditions. …”
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A Deep Learning Method for Pneumoconiosis Staging on Chest X-Ray Under Label Noise by Wenjian Sun, Dongsheng Wu, Jiang Shen, Yang Luo, Hao Wang, Li Min, Chunbo Luo

“…During the fine learning procedure, we apply two different strategies to fit the sample features in the above two subsets. …”
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Markers of tumor-associated macrophages and microglia exhibit high intratumoral heterogeneity in human glioblastoma tissue by Mikael Ispirjan, Sascha Marx, Eric Freund, Steffen K. Fleck, Joerg Baldauf, Karl Roessler, Henry W.S. Schroeder, Sander Bekeschus

“…The tissue slides were analyzed utilizing high-content imaging microscopy and algorithm-based image quantification. Several immune markers for macrophage and microglia subpopulations were investigated. …”
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Accelerometers can correctly count orthopaedic patients' early post‐operative steps while using walking aids by Spiros Tsamassiotis, Michael Schwarze, Philipp Gehring, Roman F. Karkosch, Lars‐René Tücking, Ann‐Kathrin Einfeldt, Eike Jakubowitz

“…Level of Evidence Level III diagnostic study.…”
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Prognostic and therapeutic relevance of IL2RG-related LncRNAs in clear cell renal cell carcinoma by Weijing Hu, Bo Wu, Yongquan Chen, Xiaoling Guo, Xiaosong Wang, Dongwen Wang

“…Gene set enrichment analysis (GSEA), single-sample GSEA (ssGSEA), CIBERSORT algorithms, and TISCH2 database were applied to analyze immune function and immune cell infiltration in different risk groups. …”
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Integrating bioinformatics and machine learning to uncover lncRNA LINC00269 as a key regulator in Parkinson's disease via pyroptosis pathways by LiLi Ma, Yue Liu, Yajing Ren, Na Mi, Jing Fang, Rui Bao, Xiuzhi Xu, Hongjia Zhang, Ying Tang

“…Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) recognized different lncRNAs. Through various algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Weighted Gene Co-expression Network Analysis (WGCNA), we recognized LINC01606 and LINC00269, which are key factors during the emergence and development of PD. …”
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