Comparative analysis dust accumulation impact on PV performance using artificial neural network and machine learning algorithms by Ashutosh Shukla, Rupendra Kumar Pachauri, Athar Hussain, Ahmed Ali, Baseem Khan

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Comparison of clinical nasal endoscopy, optical biopsy, and artificial intelligence in early diagnosis and treatment planning in laryngeal cancer: a prospective observational study by Ruifang Hu, Xianping Liu, Yong Zhang, Clement Arthur, Dongguang Qin

“…Direct laryngoscopy and imaging modalities are conventional diagnostic methods. However, laryngeal cancer diagnosis can be delayed, and early subtle mucosal changes can be missed. …”
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Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury by Chen Lin, Meng Zheng, Wensi Wu, Zhishan Wang, Guofeng Lu, Shaodan Feng, Xinlan Zhang

“…We assessed 113 combinations of 12 different algorithms to develop an internally and externally validated machine-learning model for diagnosing AKI. …”
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Comparison of Different HIV-1 Resistance Interpretation Tools for Next-Generation Sequencing in Italy by Daniele Armenia, Luca Carioti, Valeria Micheli, Isabella Bon, Tiziano Allice, Celestino Bonura, Bianca Bruzzone, Fiorenza Bracchitta, Francesco Cerutti, Giovanni Maurizio Giammanco, Federica Stefanelli, Maria Addolorata Bonifacio, Ada Bertoli, Marialinda Vatteroni, Gabriele Ibba, Federica Novazzi, Maria Rosaria Lipsi, Nunzia Cuomo, Ilaria Vicenti, Francesca Ceccherini-Silberstein, Barbara Rossetti, Antonia Bezenchek, Francesco Saladini, Maurizio Zazzi, Maria Mercedes Santoro

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PROGNOSTIC VALUE OF DIFFERENT OBESITY MARKERS FOR CARDIOVASCULAR RISK ASSESSMENT IN URBAN EMPLOYED MONGOLIANS by K. V. Protasov, T. Myagmarsuren

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Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region by S. I. Panin, V. A. Suvorov, A. V. Zubkov, S. A. Bezborodov, A. A. Panina, N. V. Kovalenko, A. R. Donsckaia, I. G. Shushkova, A. V. Bykov, Ya. A. Marenkov

“…Statistical processing of the data was carried out using the Python programming language. Five different classifiers were used for machine learning. …”
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Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning by Xiao Zeng, Qiong Ma, Chun-Xia Huang, Jun-Jie Xiao, Xi Fu, Yi-Feng Ren, Yu-Li Qu, Hong-Xia Xiang, Mao Lei, Ru-Yi Zheng, Yang Zhong, Ping Xiao, Xiang Zhuang, Feng-Ming You, Jia-Wei He

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The diagnostic value of two antigenic domains derived from the Trypanosoma cruzi Tc323 protein in chronic Chagas disease: a study in Brazil by Micaela S. Ossowski, Ângelo Antônio Oliveira Silva, Ângelo Antônio Oliveira Silva, Emily Ferreira Santos, Emily Ferreira Santos, Leonardo Maia Leony, Leonardo Maia Leony, Fred Luciano Neves Santos, Fred Luciano Neves Santos, Fred Luciano Neves Santos, Karina A. Gómez

“…The diagnosis of chronic infection with the parasite Trypanosoma cruzi relies on detecting specific IgG antibodies using two or three (in the case of discordance) serological assays based on different principles. This diagnostic algorithm poses challenges in identifying patients with chronic Chagas disease (CCD), particularly in endemic areas where financial and human resources are scarce. …”
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Transforming health care with artificial intelligence: Regulations, challenges, and future directions by Jalees Razavi, Hamza O. Dhafar, Ahmed S. BaHammam

“…Nevertheless, many challenges have been identified with AI’s integration in health care, including data privacy and security, algorithmic bias, and regulatory complexities. Different regulations, such as the Food and Drug Administration (FDA), General Data Protection Regulation (GDPR), European Union AI Act, and Health Insurance Portability and Accountability Act (HIPAA), played a crucial role in balancing the benefits and risks of AI, ensuring the privacy of patients’ sensitive data, ethical use of AI, accountability, transparency, and optimized patient care. …”
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Point-Of-Care low-field MRI in acute Stroke (POCS): protocol for a multicentric prospective open-label study evaluating diagnostic accuracy by Mauro Silvestrini, Simona Sacco, Massimo Caulo, Simona Marcheselli, Carmine Marini, Sergio Lucio Vinci, Angelo Galante, Marco Colasurdo, Raffaele Ornello, Matteo Foschi, Stefano Necozione, Mario Muselli, Paola Olimpia Achard, Luciano Fratocchi, Marco Cavallaro, Gabriele Polonara, Laura Straffi, Luca Sorrentino, Enrico Franconi, Marcello Alecci

“…Both units will independently adjudicate the best treatment option, while the latter will provide historical HF-MRI data to develop artificial intelligence algorithms for LF-MRI images interpretation (Free University of Bozen-Bolzano). …”
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An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images by Zhong-Yan Ma, Hai-lin Zhang, Fa-jin Lv, Wei Zhao, Dan Han, Li-chang Lei, Qin Song, Wei-wei Jing, Hui Duan, Shao-Lei Kang

“…Conclusion The AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. …”
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Atrial fibrillation risk model based on LASSO and SVM algorithms and immune infiltration of key mitochondrial energy metabolism genes by Xunjie Yang, Weng Lan, Chunyi Lin, Chunyu Zhu, Zicong Ye, Zhishi Chen, Guian Zheng

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A Robust Adaptive Strategy for Diesel Particulate Filter Health Monitoring Using Soot Sensor Data by Bilal Youssef

“…Different cost functions have been tested and evaluated to improve the diagnostic performance. …”
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Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation by Julia Geppert, Peter Auguste, Asra Asgharzadeh, Hesam Ghiasvand, Mubarak Patel, Anna Brown, Surangi Jayakody, Emma Helm, Dan Todkill, Jason Madan, Chris Stinton, Daniel Gallacher, Sian Taylor-Phillips, Yen-Fu Chen

“…Objective  To assess the accuracy, clinical effectiveness and cost-effectiveness of using  software with artificial intelligence-derived algorithms to assist in the detection and analysis of lung nodules in computed tomography scans of the chest compared with unassisted reading. …”
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Enhancing noninvasive pancreatic cystic neoplasm diagnosis with multimodal machine learning by Wei Huang, Yue Xu, Zhao Li, Jun Li, Qing Chen, Qiang Huang, Yaping Wu, Hongtan Chen

“…The study’s results indicate that our multimodal machine learning algorithm, which integrates both clinical and imaging data, significantly outperforms single-source data algorithms. …”
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Differential Diagnostic Indicators for the Appointment of Non-Steroidal Anti-Inflammatory Therapy in the Treatment of Red Eye Syndrome at the Stage of Primary Outpatient Admission.... by V. N. Trubilin, E. G. Poluninа, A. A. Kozhukhov, V. V. Kurenkov, N. V. Moreva, A. V. Trubilin, K. V. Chinenova

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Immune-related gene characterization and biological mechanisms in major depressive disorder revealed based on transcriptomics and network pharmacology by Shasha Wu, Shasha Wu, Qing Jiang, Jinhui Wang, Jinhui Wang, Daming Wu, Yan Ren

“…ROC curve analysis indicated that these hub genes possess good diagnostic value. Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) demonstrated significant expression differences of these hub genes in PBMCs between MDD patients and HCs. …”
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Retrospective Clinical Trial to Evaluate the Effectiveness of a New Tanner–Whitehouse-Based Bone Age Assessment Algorithm Trained with a Deep Neural Network System by Meesun Lee, Young-Hun Choi, Seul-Bi Lee, Jae-Won Choi, Seunghyun Lee, Jae-Yeon Hwang, Jung-Eun Cheon, SungHyuk Hong, Jeonghoon Kim, Yeon-Jin Cho

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The Evolution of Ophthalmological Healthcare System in Premature Children by A. V. Tereshhenko, I. G. Trifanenkova, M. S. Tereshhenkova, Yu. A. Yudina, S. V. Isaev, P. L. Volodin, N. N. Yudina, A. A. Vydrina, Yu. A. Sidorova, E. V. Erohina, V. V. Shaulov

“…The effective management and treatment algorithms for the patients with active ROP had not been implemented yet. …”
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The role of sodium – glucose cotransporter-2 inhibitors in the treatment of different phenotypes of chronic heart failure by M. Yu. Kolesnyk, Ya. Yu. Maistrovych

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