Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis by Martina Votto, Annalisa De Silvestri, Lorenzo Postiglione, Maria De Filippo, Sara Manti, Stefania La Grutta, Gian Luigi Marseglia, Amelia Licari

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Enhanced Detection and Segmentation of Brain Tumors Using a Dense BW-CNN Approach by Sangeeta Kakarwal, Rahul Mapari, Anuj Kumar Jain, Vipin Sharma, Pranoti Prashant Mane, Jambi Ratna Raja Kumar, Ranjan K. Pradhan

“…These results highlight the potential of the BW-DCNN approach to significantly advance the capabilities of computer-aided diagnostic systems in medical imaging, offering a promising direction for future research and application in clinical settings.…”
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Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD by Miao-Lin Lei, Guan-Wei Bi, Xiao-Lin Yin, Xiao-Lin Yin, Yue Wang, Yue Wang, Yue Wang, Zi-Ru Sun, Zi-Ru Sun, Zi-Ru Sun, Xin-rui Guo, Hui-peng Zhang, Xiao-han Zhao, Feng Li, Feng Li, Yan-Bo Yu

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Nondestructive detection of sweet potato leaf curl virus using 3D laser imaging combined with deep learning by Yican Yang, Nuwan K. Wijewardane, Tyler J. Slonecki, Phillip A. Wadl, Sharon A. Andreason, Jingdao Chen, Lorin Harvey

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Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases by Benjamin Böttcher, Marly van Assen, Roberto Fari, Philipp L. von Knebel Doeberitz, Eun Young Kim, Eugene A. Berkowitz, Felix G. Meinel, Carlo N. De Cecco

“…Dataset 1 was used for sessions A and C, assessing diagnostic accuracy and confidence with mandatory and without CBIR software. …”
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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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THE PERCENTAGE AND STRUCTURE OF PERODONT TISSUE DISEASES IN PATIENTS OF LVIV REGIONAL STATIONARY CLINICAL HOSPITAL DEPENDING ON AGE by M. М. Shevchuk

“…To study the prevalence and structure of periodontal tissue lesions in patients with general - somatic diseases in order to develop a therapeutic algorithm in a hospital setting. Materials and methods. …”
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Creating a retinal image database to develop an automated screening tool for diabetic retinopathy in India by Ramachandran Rajalakshmi, Thyparambil Aravindakshan PramodKumar, Ashis Kumar Dhara, Geetha Kumar, Naziya Gulnaaz, Shramana Dey, Sourav Basak, B Uma Shankar, Raka Goswami, Raja Mohammed, Suchetha Manikandan, Sushmita Mitra, Harsimran Thethi, Saravanan Jebarani, Sinnakaruppan Mathavan, Tamilselvi Sarveswaran, Ranjit Mohan Anjana, Viswanathan Mohan, Sambuddha Ghosh, Tushar Kanti Bera, Rajiv Raman

“…Additionally, the accurate training of these AI algorithms necessitates skilled professionals, such as optometrists or ophthalmologists, to provide reliable ground truths that ensure the precision of the diagnostic outputs. …”
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Factors associated with tuberculosis drug resistance among presumptive multidrug resistance tuberculosis patients identified in a DRTB surveillance study in western Kenya by Albert Okumu, James Orwa, Ruth Sitati, Isaiah Omondi, Ben Odhiambo, Jeremiah Ogoro, George Oballa, Benjamin Ochieng, Steve Wandiga, Collins Ouma

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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…This fusion of deep learning innovation with the principles of randomization-based algorithms opens new avenues for developing accessible, accurate diagnostic tools, potentially alleviating the global impact of diabetic vision loss.…”
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Recommendations for All-Round Newborns and Infants Hearing Screening in Russian Federation by S. S. Chibisova, G. S. Tufatulin, L. S. Namazova-Baranova, I. V. Koroleva, E. R. Tsygankova, T. G. Markova, N. N. Volodin, G. A. Tavartkiladze

“…The availability and correct function of hearing research equipment, specialists’ qualifications, study technique and conditions, documents and results transmitting accuracy were estimated. 2) Updating the list of risk factors of hearing loss. …”
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Interpretive prediction of hyperuricemia and gout patients via machine learning analysis of human gut microbiome by Jia-Wei Tang, Alfred Chin Yen Tay, Liang Wang

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Role of clinical evaluation in the identification of coronary obstructive disorders in patients with stable coronary artery disease. Part I by A. N. Sumin

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Enhanced Neutrosophic Set and Machine Learning Approach for Breast Cancer Prediction by Ashika T, Hannah Grace, Nivetha Martin, Florentin Smarandache

“…Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the research transforms these data into Neutrosophic (N) representations to effectively capture uncertainties. …”
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Identifying People Living With or Those at Risk for HIV in a Nationally Sampled Electronic Health Record Repository Called the National Clinical Cohort Collaborative: Computational... by Eric Hurwitz, Cara D Varley, A Jerrod Anzolone, Vithal Madhira, Amy L Olex, Jing Sun, Dimple Vaidya, Nada Fadul, Jessica Y Islam, Lesley E Jackson, Kenneth J Wilkins, Zachary Butzin-Dozier, Dongmei Li, Sandra E Safo, Julie A McMurry, Pooja Maheria, Tommy Williams, Shukri A Hassan, Melissa A Haendel, Rena C Patel

“… BackgroundElectronic health records (EHRs) provide valuable insights to address clinical and epidemiological research concerning HIV, including the disproportionate impact of the COVID-19 pandemic on people living with HIV. …”
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Identifying the risk of Kawasaki disease based solely on routine blood test features through novel construction of machine learning models by Tzu-Hsien Yang, Ying-Hsien Huang, Yuan-Han Lee, Jie-Nan Lai, Kuang-Den Chen, Mindy Ming-Huey Guo, Yan Pan, Chun-Yu Chen, Wei-Sheng Wu, Ho-Chang Kuo

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Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data by Shehu Mohammed, Neha Malhotra

“…The importance of this work is in the opportunity to shift diagnostic paradigms by employing deep learning algorithms, including CNNs, LSTM networks, and GNNs to analyze spatial, temporal, and relational patterns across multi-modal data. …”
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Preoperative MRI-based radiomics analysis of intra- and peritumoral regions for predicting CD3 expression in early cervical cancer by Rui Zhang, Chunfan Jiang, Feng Li, Lin Li, Xiaomin Qin, Jiang Yang, Huabing Lv, Tao Ai, Lei Deng, Chencui Huang, Hui Xing, Feng Wu

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Analysis of Gene Expression Omnibus high-throughput sequencing data for the determination of microribonucleic acids in the blood plasma of patients with glioblastomas by A. A. Pushkin, D. Yu. Gvaldin, N. N. Timoshkina, E. E. Rostorguev, L. Yu. Vladimirova, E. A. Dzenkova

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Advancements in Parkinson’s Disease Diagnosis: A Comprehensive Survey on Biomarker Integration and Machine Learning by Ruchira Pratihar, Ravi Sankar

“…This comprehensive review explores the advancements in machine learning algorithms in the diagnosis of Parkinson’s disease (PD) utilizing different biomarkers. …”
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