Differential diagnosis of hematogenous osteomyelitis and malignant bone tumors by A. N. Tkachenko, O. F. Nur, A. A. Korneenkov, L. E. Sharova, I. E. Itskovich, I. I. Kushnirchuk, A. Yu. Cherkasov

“…Our differential diagnostic model was tested in the prospective study (63 cases). …”
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Unpacking the dangers of super absorbent polymer water beads: an in vitro analysis by Jack J. Hachem, Jack J. Hachem, Javier Monagas, Javier Monagas, Ankona Banerjee, Robert A. Noel, Robert A. Noel

“…Our findings underscore the need for standardized management of SAP bead ingestion, which could improve patient outcomes while reducing unnecessary diagnostic and therapeutic interventions. Further research and clinical validation of these strategies in vivo are essential to develop safe and efficient protocols for managing SAP bead ingestions…”
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Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia by Mahwish Ilyas, Muhammad Bilal, Nadia Malik, Hikmat Ullah Khan, Muhammad Ramzan, Anam Naz

“…This research study proposes an automatic diagnostics prediction of leukemia by analyzing blood images according to the shape of the blast cells using digital image processing and machine learning. …”
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Analysis of Microbiome for AP and CRC Discrimination by Alessio Rotelli, Ali Salman, Leandro Di Gloria, Giulia Nannini, Elena Niccolai, Alessio Luschi, Amedeo Amedei, Ernesto Iadanza

“…Transitioning from this exploration, the synergy of ML and synthetic data enrichment holds promise for advancing microbiome research. This approach enhances classification accuracy and reveals hidden microbial markers that could prove valuable in clinical practice as a diagnostic and prognostic tool.…”
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Ensemble network using oblique coronal MRI for Alzheimer’s disease diagnosis by Cunhao Li, Zhongjian Gao, Xiaomei Chen, Xuqiang Zheng, Xiaoman Zhang, Chih-Yang Lin

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A Machine-Learning Model for the Prediction of Triple-Negative Breast Cancer Based on Multiparameter MRI by Cai Y, Li Y, Wang W, Zhou Y, Wang J, Zhang L, Lu H

“…Yuxin Cai,1,* Yanbo Li,1,* Wenqi Wang,1 Yaqiu Zhou,1 Jingbo Wang,1 Lina Zhang,1,2 Hong Lu1 1Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People’s Republic of China; 2Second Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hong Lu, Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People’s Republic of China, Tel +86-18622221319, Email honglu@tmu.edu.cnObjective: To explore the difference between triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC) based on multi-parametric MRI imaging features and construct a prediction model to identify TNBC.Methods: A retrospective study enrolled 1353 women with 1376 malignant lesions who had no additional therapy before surgery between January 2019 and December 2020 in a single center. …”
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Differentiation of multiple adrenal adenoma subtypes based on a radiomics and clinico-radiological model: a dual-center study by Xinzhang Zhang, Yapeng Si, Xin Shi, Yiwen Zhang, Liuyang Yang, Junfeng Yang, Ye Zhang, Jinjun Leng, Pingping Hu, Hao Liu, Jiaqi Chen, Wenliang Li, Wei Song, Jianping Zhu, Maolin Yang, Wei Li, Junfeng Wang

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Artificial intelligence in primary aldosteronism: current achievements and future challenges by Yisi Xu, Benjin Liu, Xuqi Huang, Xudong Guo, Ning Suo, Shaobo Jiang, Hanbo Wang

“…In subtype classification, AI algorithms achieve high predictive accuracy in differentiating PA subtypes through comprehensive analysis of clinical, imaging, and biochemical data, while simultaneously reducing reliance on invasive diagnostic procedures. …”
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Cloud-magnetic resonance imaging system: In the era of 6G and artificial intelligence by Yirong Zhou, Yanhuang Wu, Yuhan Su, Jing Li, Jianyu Cai, Yongfu You, Jianjun Zhou, Di Guo, Xiaobo Qu

“…Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. …”
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Personalized Intervention Strategy Based on a Risk Score Generated From Subcutaneous Insertable Cardiac Monitor: Results From Phase 1 of ALLEVIATE‐HF by Rami Kahwash, Michael R. Zile, Prasad Chalasani, Barry Bertolet, Laura Gravelin, Muhammad Shahzeb Khan, Jennifer Wehking, Brian Van Dorn, Shantanu Sarkar, Verla Laager, Bart Gerritse, Aimee Laechelt, Javed Butler

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Anorexia-cachexia syndrome in cancer patients: pathogenetic aspects and treatment options by A. V. Snegovoy, V. B. Larionova, I. V. Kononenko

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Intermittent hypoxemia during hemodialysis: AI-based identification of arterial oxygen saturation saw-tooth pattern by Hanjie Zhang, Andrea Nandorine Ban, Peter Kotanko

“…Conclusion Our 1D-CNN algorithm accurately classifies SaO2 saw-tooth pattern. …”
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Congenital Ichthyosis: Clinical and Genetic Characteristics of the Disease by Nikolay N. Murashkin, Karine O. Avetisyan, Roman A. Ivanov, Svetlana G. Makarova

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Harnessing Microbiome, Bacterial Extracellular Vesicle, and Artificial Intelligence for Polycystic Ovary Syndrome Diagnosis and Management by Bhawna Kushawaha, Tial T. Rem, Emanuele Pelosi

“…We explore the diagnostic potential of the AI-driven analysis of microbiome and BEV profiles, and address ethical considerations including data privacy and algorithmic bias. …”
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Radiomics-based machine learning for automated detection of Pneumothorax in CT scans. by Hanieh Alimiri Dehbaghi, Karim Khoshgard, Hamid Sharini, Samira Jafari Khairabadi, Farhad Naleini

“…By enhancing the detection of pneumothorax, this research aims to mitigate diagnostic errors and accelerate the process of image interpretation, ultimately improving patient outcomes. …”
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Application of artificial intelligence in the diagnosis and treatment of lacrimal disorders: challenges and opportunities by PENG Xintong, LI Guangyu

“…Lacrimal disorders are common ophthalmic conditions characterized by complex diagnostic and treatment processes, involving intricate therapeutic approaches and diverse clinical and imaging data. …”
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MYOFASCIAL SYMPTOMS IN CLINIC COMPRESSION OF DISLOCATION OF THE TEMPOROMANDIBULAR JOINT DYSFUNCTION by V.M. Novikov, P.I. Yatsenko, O.V. Rybalov

“…The issue here is that the algorithm of diagnostic measures with paresis of mimetic muscles does not include the research of TMJ components´ correlation between each other. …”
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Organ‐based clues for diagnosis of inborn errors of immunity: A practical guide for clinicians by Fatemeh Mohammadi, Amirhossein Yadegar, Mahta Mardani, Aryan Ayati, Hassan Abolhassani, Nima Rezaei

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IKZF1 as a potential therapeutic target for dendritic cell-mediated immunotherapy in IgA nephropathy by Fei Peng, Chunjia Sheng, Jiayi He, Yena Zhou, Yilun Qu, Shuwei Duan, Yinghua Zhao, Jikai Xia, Jie Wu, Guangyan Cai, Lingling Wu, Chuyue Zhang, Xiangmei Chen

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Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review by Émile Lemoine, Joel Neves Briard, Bastien Rioux, Oumayma Gharbi, Renata Podbielski, Bénédicte Nauche, Denahin Toffa, Mark Keezer, Frédéric Lesage, Dang K. Nguyen, Elie Bou Assi

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