Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review by Rim Missaoui, Wided Hechkel, Wajdi Saadaoui, Abdelhamid Helali, Marco Leo

“…In the light of recent developments in brain tumor analysis, many algorithms have been proposed to accurately obtain ontological characteristics of tumors, enhancing diagnostic precision and personalized therapeutic strategies.…”
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Accurate and rapid single nucleotide variation detection in PCSK9 gene using nanopore sequencing by Ilaria Massaiu, Vincenza Valerio, Valentina Rusconi, Valentina Rusconi, Francesca Bertolini, Donato De Giorgi, Veronika A. Myasoedova, Paolo Poggio, Paolo Poggio

“…Twelve subjects were analyzed using different sequencing flow cells, basecalling models, and SNV calling algorithms. …”
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Application of Internet Hospitals in the Disease Management of Patients With Ulcerative Colitis: Retrospective Study by Tianzhi Yu, Wanyu Li, Yingchun Liu, Chunjie Jin, Zimin Wang, Hailong Cao

“…Intelligent diagnosis refers to the use of artificial intelligence–driven algorithms to analyze patient-reported symptoms, generate diagnostic probabilities, and provide treatment recommendations through interactive tools. …”
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Accurate bladder cancer diagnosis using ensemble deep leaning by Rana A. El-Atier, M. S. Saraya, Ahmed I. Saleh, Asmaa H. Rabie

“…On the other hand, if the three methods give different class category, then the final result will be followed by the accuracy of each class. …”
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Accuracy and consequences of using trial-of-antibiotics for TB diagnosis (ACT-TB study): protocol for a randomised controlled clinical trial by Neil French, Peter MacPherson, Titus Henry Divala, Katherine L Fielding, Marriott Nliwasa, Chikondi Charity Kandulu, Lingstone Chiume, Sanderson Chilanga, Masiye John Ndaferankhande

“…Introduction Over 40% of global tuberculosis case notifications are diagnosed clinically without mycobacteriological confirmation. Standard diagnostic algorithms include ‘trial-of-antibiotics’—empirical antibiotic treatment given to mycobacteriology-negative individuals to treat infectious causes of symptoms other than tuberculosis, as a ‘rule-out’ diagnostic test for tuberculosis. …”
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The Magnified Waltz: Simulating Light Curves of Binary Stars Passing Through Microcaustics in Strong Lensing Galaxy Clusters by Wenwen Zheng, Xiaoting Fu, Yang Chen, Xuefei Chen, Yanjun Guo, Xuechun Chen, Huanyuan Shan, Guoliang Li

“…Our simulations reveal that binary stars produce diverse light-curve features, including overlapping peaks, plateau-like structures, and time-variable color–magnitude differences. These features, particularly the distinct temporal variations in spectral energy distributions, offer diagnostic tools for distinguishing binary systems from single stars. …”
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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…Abstract Background The accurate diagnosis of thyroid nodules represents a critical and frequently encountered challenge in clinical practice, necessitating enhanced precision in diagnostic methodologies. In this study, we investigate the predictive efficacy of distinguishing between benign and malignant thyroid nodules by employing traditional machine learning algorithms and a deep transfer learning model, aiming to advance the diagnostic paradigm in this field. …”
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Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data by Quan Zhou, Lihua Zhang, Nan Xiang, Lele Zhang, Fuqiang Ma, Fengchang Yu, Shenhui Lv, Zhilin Lu, He-Rong Mao

“…In the multivariate LR analysis, statistically significant differences existed between the TNs group and thyroid cancer group in gender, age, free triiodothyronine (FT3), free thyroxine (FT4) and TPOAB. …”
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Liquid biopsy-derived extracellular vesicle protein biomarkers for diagnosis and prognostic assessment of lung squamous cell carcinoma by Sheng Ma, Na Zhao, Xin Dong, Yaru Wang, Lei Song, Ruiqi Zheng, Xiaochen Zhi, Congcong Ma, Shujun Cheng, Jie Li, Yutao Liu, Ting Xiao

“…Validation was conducted through transmission electron microscopy, nanoparticle tracking analyses, and Western blotting. Machine learning algorithms were utilized to compute protein biomarkers associated with LUSC and establish a diagnostic model. …”
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A Data-Driven Comparative Analysis of Machine-Learning Models for Familial Hypercholesterolemia Detection by Tomasz Kocejko

“…This study presents an assessment of familial hypercholesterolemia (FH) probability using different algorithms (CatBoost, XGBoost, Random Forest, SVM) and its ensembles, leveraging electronic health record data. …”
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Research on a Burn Severity Detection Method Based on Hyperspectral Imaging by Sijia Wang, Minghui Gu, Mingle Zhang, Xin Tan

“…The accurate detection of burn wounds is a key research direction in the field of burn medicine, as diagnostic results directly influence the risk of wound infection and the formation of hypertrophic scars. …”
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A novel spectral transformation technique based on special functions for improved chest X-ray image classification. by Abeer Aljohani

“…Chest X-ray image classification plays an important role in medical diagnostics. Machine learning algorithms enhanced the performance of these classification algorithms by introducing advance techniques. …”
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A comparative study of bone density in elderly people measured with AI and QCT by Min Guo, Min Guo, Yu Zhang, Yu Zhang, XinXin Gu, XinXin Gu, Xuhui Liu, Xuhui Liu, Fei Peng, Fei Peng, Zongjun Zhang, Zongjun Zhang, Mei Jing, Mei Jing, Yingxia Fu, Yingxia Fu

“…The linear regression fit between the R2 values of QCT and Bone Density AI for measuring lumbar spine BMD with different equipment ranged from 0.88 to 0.96, indicating a high degree of consistency between the two measurement methods across devices.ConclusionThis multicenter study pioneers a dual-validation framework to establish the clinical validity of deep learning-based BMD prediction algorithms using routine thoracic/abdominal CT scans. …”
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An Integrated Strategy for Interpretable Fault Diagnosis of UAV EHA DC Drive Circuits Under Early Fault and Imbalanced Data Conditions by Yang Li, Zhen Jia, Jie Liu, Kai Wang, Peng Zhao, Xin Liu, Zhenbao Liu

“…The performance of the proposed diagnostic strategy is fully verified by setting up different comparison algorithms in two experimental circuits. …”
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A review of machine learning and deep learning for Parkinson’s disease detection by Hajar Rabie, Moulay A. Akhloufi

“…Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). …”
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In Infants with Neuroblastoma Standard Therapy Only Partially Reverts the Fecal Microbiome Dysbiosis Present at Diagnosis by Maria Valeria Corrias, Eddi Di Marco, Carola Bonaretti, Margherita Squillario, Loredana Amoroso, Massimo Conte, Mirco Ponzoni, Roberto Biassoni

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Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis by Jiarui Wang, Shiyan Jiang, Jiaxin Shi, Jing Wang, Shengrong Du, Zufang Huang

“…Semen samples from healthy controls ([Formula: see text]) and azoospermic patients ([Formula: see text]) were collected, and their exosomal SERS spectra were obtained. Machine learning algorithms were employed to distinguish between the SERS profiles of healthy and azoospermic samples, achieving an impressive sensitivity of 99.61% and a specificity of 99.58%, thereby highlighting significant spectral differences. …”
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Identification and Validation of Aging Related Genes Signature in Chronic Obstructive Pulmonary Disease by Tian-Tian Li, Hong-Yan Bai, Jing-Hong Zhang, Xiu-He Kang, Yi-Qing Qu

“…The SVM-RFE and LASSO algorithms pinpointed four potential diagnostic biomarkers. …”
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Transcriptomic Signatures of Antibody-mediated Rejection in Early Biopsies With Negative Histology in HLA-incompatible Kidney Transplantation by Petra Hruba, PhD, Jiri Klema, PhD, Petra Mrazova, PhD, Eva Girmanova, PhD, Katerina Jaklova, MS, Ludek Voska, MD, Martin Kment, MD, Martina Mackova, PhD, Klara Osickova, MD, Vladimir Hanzal, MD, Philip F. Halloran, MD, PhD, Ondrej Viklicky, MD, PhD

“…RNA sequencing from biopsies selected from the biobank was used in cohort 1 (n = 32) and microarray, including the molecular microscope (Molecular Microscope Diagnostic System [MMDx]) algorithm, in recent cohort 2 (n = 30). …”
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FUSCANet: Enhancing Skin Disease Classification Through Feature Fusion and Spatial-Channel Attention Mechanisms by Qinyang Liu, Xuan Wang, Hongjiu Liu, Xiangzhen Zang, Lei Li, Zhanlin Ji, Ivan Ganchev

“…With the widespread application of computer vision technology in dermatology, automating skin lesion classification through computer algorithms has become a crucial method for improving diagnostic efficiency and reducing the mortality rate due to malignant skin conditions. …”
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