Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study by Ran Zhang, Tian Li, Fan Fan, Haoying He, Liuyi Lan, Dong Sun, Zhipeng Xu, Sisi Peng, Jing Cao, Juan Xu, Xiaoxiang Peng, Ming Lei, Hao Song, Junjian Zhang

“…The final model also achieved, and marginally exceeded, clinician-level diagnostic performance. Conclusions Our research established a consistent and explainable ML framework for identifying VaDep in older adults, utilizing comprehensive clinical data. …”
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Integrating multi-omics and machine learning strategies to explore the “gene-protein-metabolite” network in ischemic heart failure with Qi deficiency and blood stasis syndrome by Jingjing Wei, Aolong Wang, Peng Yu, Yang Sun, Wenjun Wu, Yilin Zhang, Rui Yu, Bin Li, Mingjun Zhu

“…This biomarker combination significantly enhanced the diagnostic performance of IHF-QXXY syndrome (AUC > 0.863) and retained high diagnostic accuracy during validation (AUC > 0.75). …”
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An ensemble machine learning framework with explainable artificial intelligence for predicting haemoglobin anaemia considering haematological markers by Dhruva Darshan B S, Punit Sharma, Krishnaraj Chadaga, Niranjana Sampathila, G. Muralidhar Bairy, Sushma Belurkar, Srikanth Prabhu, Swathi K S

“…The algorithms are further ensembled using a customized stacking approach. …”
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On a New Modification of the Weibull Model with Classical and Bayesian Analysis by Yen Liang Tung, Zubair Ahmad, Omid Kharazmi, Clement Boateng Ampadu, E.H. Hafez, Sh. A.M. Mubarak

“…Finally, considering the failure times data, the Bayesian analysis and performance of Gibbs sampling are discussed. The diagnostics measures such as the Raftery–Lewis, Geweke, and Gelman–Rubin are applied to check the convergence of the algorithm.…”
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Explainable AI-Based Skin Cancer Detection Using CNN, Particle Swarm Optimization and Machine Learning by Syed Adil Hussain Shah, Syed Taimoor Hussain Shah, Roa’a Khaled, Andrea Buccoliero, Syed Baqir Hussain Shah, Angelo Di Terlizzi, Giacomo Di Benedetto, Marco Agostino Deriu

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In the Refractory Hypertension “Labyrinth”. Focus on Primary Hyperaldosteronism by O. V. Tsygankova, T. I. Batluk, L. D. Latyntseva, E. V. Akhmerova, N. M. Akhmedzhanov

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Uncovering Hippo pathway-related biomarkers in acute myocardial infarction via scRNA-seq binding transcriptomics by Xingda Li, Xueqi He, Yu Zhang, Xinyuan Hao, Anqi Xiong, Jiayu Huang, Biying Jiang, Zaiyu Tong, Haiyan Huang, Lian Yi, Wenjia Chen

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Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review by Mohammad Faiz, Bakkanarappa Gari Mounika, Mohd Akbar, Swapnita Srivastava

“…The primary objective of this research is to investigate automated techniques that can be employed to detect ALL at an early stage. …”
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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

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Investigation of predictive factors for fatty liver in children and adolescents using artificial intelligence by Aliakbar Sayyari, Amin Magsudy, Yasamin Moeinipour, Amirhossein Hosseini, Hamidreza Amiri, Mohammadreza Arzaghi, Fereshteh Sohrabivafa, Seyedeh Fatemeh Hamzavi, Ashkan Azizi, Tahereh Hatamii, AmirAli Okhovat, Naghi Dara, Negar Imanzadeh, Farid Imanzadeh, Mahmoud Hajipour

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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

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THROMBOPHILIA AS A FACTOR OF ETHIOLOGY AND PATHOGENESYS OF DISORDERS IN THE SYSTEM “WOMAN – FETUS – NEWBORN” by A. Ya. Il'ina, P. V. Shumilov, A. L. Mishchenko, A. S. Barinova, I. Yu. Kokaya, V. N. Kashin, O. S. Pobedinskaya, I. V. Solov'eva, T. S. Kharlamova, A. V. Budancev, E. A. Akhalova

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Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning by Xianchang Zeng, Lingyun Wei, Lu Lv, Di Wu, Yingying Shen, Xinliang Lu, Xianghui Kong, Zhijian Cai, Jianli Wang, Jianli Wang

“…Single-cell RNA sequencing was further employed to evaluate the heterogeneity of immune cells. Machine learning algorithms were utilized to construct and identify diagnostic markers for LUAD, which were then validated by receiver operating characteristic (ROC) curve analysis. …”
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Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences by Eugenia Mylona, Dimitrios I. Zaridis, Charalampos Ν. Kalantzopoulos, Nikolaos S. Tachos, Daniele Regge, Nikolaos Papanikolaou, Manolis Tsiknakis, Kostas Marias, ProCAncer-I Consortium, Dimitrios I. Fotiadis

“…Critical relevance statement This work may guide future radiomic research, paving the way for the development of more effective and reliable radiomic models; not only for advancing prostate cancer diagnostic strategies, but also for informing broader applications of radiomics in different medical contexts. …”
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Awareness and Perspectives on the Role of Artificial Intelligence in Primary Care: Survey of Rural and Urban Primary Care Physicians in Alberta, Canada by Jose Uriel Perez

“…This survey's findings may inform future research into the development and implementation of AI in primary care. …”
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Speech Analysis as a Tool for Detection and Monitoring of Medical Conditions: A review by Magdalena IGRAS-CYBULSKA, Daria HEMMERLING, Mariusz ZIÓŁKO, Wojciech DATKA, Ewa STOGOWSKA, Michał KUCHARSKI, Rafał RZEPKA, Bartosz ZIÓŁKO

“…Advanced computer voice analysis with machine learning algorithms combined with the widespread availability of smartphones allows diagnostic analysis to be conducted during the patient’s visit to the doctor or at the patient’s home during a telephone conversation. …”
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Classification of mammograms: Comparing a graphical to a geometrical approach by Anirban Ghosh, Priya Ranjan, Kumar Dron Shrivastav, Rajiv Janardhanan

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Applications of artificial intelligence in thoracic imaging: a review by Arjun Kalyanpur, Neetika Mathur

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Artificial Intelligence in Pediatric Orthopedics: A Comprehensive Review by Andrea Vescio, Gianluca Testa, Marco Sapienza, Filippo Familiari, Michele Mercurio, Giorgio Gasparini, Sergio de Salvatore, Fabrizio Donati, Federico Canavese, Vito Pavone

“…For developmental dysplasia of the hip, deep learning algorithms demonstrated high diagnostic performance in radiographic interpretation. …”
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Rationale and design of the HERZCHECK trial: Detection of early heart failure using telemedicine and cardiovascular magnetic resonance in structurally weak regions (NCT05122793) by Sebastian Kelle, Anna Clara Nolden, Maximilian Leo Müller, Rebecca Elisabeth Beyer, Henning Steen, Bjoern Andrew Remppis, Johannes Wieditz, Hannah Kentenich, Alex Tuit, Mina Cvetkovic, Undine Ella Witt, Florian André, Sein Schmidt, Alexander Huppertz, Dusan Simic, Dirk Müller, Arim Shukri, Matthias Issing, Andre Glardon, Katrin Christiane Reber, Ulf Landmesser, Norbert Frey, Burkert Pieske, Stephanie Stock, Volkmar Falk, Tim Friede, Gisela Thiede

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