Screening and Risk Analysis of Atrial Fibrillation After Radiotherapy for Breast Cancer: Protocol for the Cross-Sectional Cohort Study “Watch Your Heart (WATCH)” by Laura Saint-Lary, Baptiste Pinel, Loic Panh, Gaelle Jimenez, Julien Geffrelot, Youlia Kirova, Jeremy Camilleri, David Broggio, Marie-Odile Bernier, Corinne Mandin, Christelle Levy, Serge Boveda, Juliette Thariat, Sophie Jacob

“…Identifying the dose-risk associations would improve RT delivery protocols to limit the occurrence of different forms of AF and, if necessary, initiate appropriate treatment. …”
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Design and Implementation of a Deep Learning-Based Hand Gesture Recognition System for Rehabilitation Internet-of-Things (RIoT) Environments Using MediaPipe by Nurul Hanis Mohd Dhuzuki, Ahmad Anwar Zainuddin, Nur Anis Sofea Kamarul Zaman, Alin Nur Maisarah Ahmad Razmi, Wonderful Shammah Kaitane, Asmarani Ahmad Puzi, Mohd Naqiuddin Johar, Maslina Yazid, Nor Azlin Mohd Nordin, Shahrul Naim Sidek, Hasan Firdaus Mohd Zaki

“…Second, accuracy is improved by integrating MediaPipe with deep learning techniques. …”
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Preoperatively-determined Red Distribution Width (RDW) predicts prolonged length of stay after single-level spinal fusion in elderly patients by Anton Früh, Dietmar Frey, Adam Hilbert, Claudius Jelgersma, Christian Uhl, Nitzan Nissimov, Peter Truckenmüller, David Wasilewski, Dimitrios Rallios, Matthias Hoppe, Simon Bayerl, Nils Hecht, Peter Vajkoczy, Lars Wessels

“…Classical correlation analysis, Receiver-operating characteristic (ROC) and new machine learning algorithms) were used. Results: A total of 208 patients were included in the present study. …”
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Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics by Gangfeng Zhu, Yipeng Song, Zenghong Lu, Qiang Yi, Rui Xu, Yi Xie, Shi Geng, Na Yang, Liangjian Zheng, Xiaofei Feng, Rui Zhu, Xiangcai Wang, Li Huang, Yi Xiang

“…Abstract Background Metabolic dysfunction-associated steatotic liver disease (MASLD) is a global health concern that necessitates early screening and timely intervention to improve prognosis. The current diagnostic protocols for MASLD involve complex procedures in specialised medical centres. …”
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Construction of a sugar and acid content estimation model for Miliang-1 kiwifruit during storage by LIU Li, YANG Tianyi, DONG Congying, SHI Caiyun, SI Peng, WEI Zhifeng, GAO Dengtao

“…To select the optimal hyperspectral wavelengths for predicting kiwifruit quality, Genetic Algorithm (GA) and Random Frog (RF) methods were employed. …”
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End-to-end deep fusion of hyperspectral imaging and computer vision techniques for rapid detection of wheat seed quality by Tingting Zhang, Jing Li, Jinpeng Tong, Yihu Song, Li Wang, Renye Wu, Xuan Wei, Yuanyuan Song, Rensen Zeng

“…Real-time monitoring and pre-sowing screening offer substantial potential for improved storage management, field performance, and flour quality. …”
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AI models for the identification of prognostic and predictive biomarkers in lung cancer: a systematic review and meta-analysis by Hind M. AlOsaimi, Aseel M. Alshilash, Layan K. Al-Saif, Jannat M. Bosbait, Roaa S. Albeladi, Dalal R. Almutairi, Alwaleed A. Alhazzaa, Tariq A. Alluqmani, Saud M. Al Qahtani, Sara A. Almohammadi, Razan A. Alamri, Abdullah A. Alkurdi, Waleed K. Aljohani, Raghad H. Alraddadi, Mohammed K. Alshammari

“…AI models, particularly deep learning and machine learning algorithms demonstrated high accuracy in predicting biomarker status. …”
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Predictive modeling and interpretative analysis of risks of instability in patients with Myasthenia Gravis requiring intensive care unit admission by Chao-Yang Kuo, Emily Chia-Yu Su, Hsu-Ling Yeh, Jiann-Horng Yeh, Hou-Chang Chiu, Chen-Chih Chung

“…This novel, personalized approach to risk stratification elucidates crucial risk factors and has the potential to enhance clinical decision-making, optimize resource allocation, and ultimately improve patient outcomes.…”
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Systemic longitudinal immune profiling identifies proliferating Treg cells as predictors of immunotherapy benefit: biomarker analysis from the phase 3 CONTINUUM and DIPPER trials by Sai-Wei Huang, Wei Jiang, Sha Xu, Yuan Zhang, Juan Du, Ya-Qin Wang, Kun-Yu Yang, Ning Zhang, Fang Liu, Guo-Rong Zou, Feng Jin, Hai-Jun Wu, Yang-Ying Zhou, Xiao-Dong Zhu, Nian-Yong Chen, Cheng Xu, Han Qiao, Na Liu, Ying Sun, Jun Ma, Ye-Lin Liang, Xu Liu

“…This study presents biomarker analyses from the phase 3 CONTINUUM trial (NCT03700476), the first to show that adding anti-PD-1 (aPD1) to chemoradiotherapy (CRT) improves event-free survival (EFS) in patients with locoregionally advanced nasopharyngeal carcinoma. …”
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Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens by Paul English, Muir J. Morrison, Blaine Mathison, Elizabeth Enrico, Ryan Shean, Brendan O'Fallon, Deven Rupp, Katie Knight, Alexandra Rangel, Jeffrey Gilivary, Amanda Vance, Haleina Hatch, Leo Lin, David P. Ng, Salika M. Shakir

“…By building on our work, researchers can develop better algorithms to improve the diagnosis of AFB, reducing the burden on laboratory staff and improving diagnostic speed and accuracy of these medically important organisms.…”
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Evaluating the Performance and Potential Bias of Predictive Models for Detection of Transthyretin Cardiac Amyloidosis by Jonathan Hourmozdi, MD, MSAI, MA, Nicholas Easton, MSAI, Simon Benigeri, MSAI, James D. Thomas, MD, Akhil Narang, MD, David Ouyang, MD, Grant Duffy, BS, Ross Upton, PhD, Will Hawkes, PhD, Ashley Akerman, PhD, Ike Okwuosa, MD, Adrienne Kline, MD, PhD, Abel N. Kho, MD, Yuan Luo, PhD, Sanjiv J. Shah, MD, Faraz S. Ahmad, MD, MS

“…Screening for ATTR-CM with artificial intelligence and other algorithms may improve timely diagnosis, but these algorithms have not been directly compared. …”
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From data to nutrition: the impact of computing infrastructure and artificial intelligence by Pierpaolo Di Bitonto, Michele Magarelli, Pierfrancesco Novielli, Donato Romano, Domenico Diacono, Lorenzo de Trizio, Angelo Mariano, Claudia Zoani, Riccardo Ferrero, Alessandra Manzin, Maria De Angelis, Roberto Bellotti, Sabina Tangaro

“…Future research should focus on refining AI models to improve their reliability and exploring how to integrate these technologies into everyday nutritional practices for better health outcomes.…”
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Computer Viewing Model for Classification of Erythrocytes Infected with <i>Plasmodium</i> spp. Applied to Malaria Diagnosis Using Optical Microscope by Eduardo Rojas, Irene Cartas-Espinel, Priscila Álvarez, Matías Moris, Manuel Salazar, Rodrigo Boguen, Pablo Letelier, Lucia San Martín, Valeria San Martín, Camilo Morales, Neftalí Guzmán

“…The use of artificial intelligence to identify morphological patterns in erythrocytes would improve our diagnostic capability. The object of this study was therefore to establish computer viewing models able to classify blood cells infected with <i>Plasmodium</i> spp. to support malaria diagnosis by optical microscope. …”
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Evaluating the performance of herd-specific long short-term memory models to identify automated health alerts associated with a ketosis diagnosis in early-lactation cows by N. Taechachokevivat, B. Kou, T. Zhang, M.E. Montes, J.P. Boerman, J.S. Doucette, R.C. Neves

“…Currently, various commercial systems offer built-in alert algorithms to identify cows requiring attention. To our knowledge, no work has been done to compare the use of models accounting for herd-level variability on their predictive ability against automated systems. …”
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Deep Learning Classification of Systemic Sclerosis Skin Using the MobileNetV2 Model by Metin Akay, Yong Du, Cheryl L. Sershen, Minghua Wu, Ting Y. Chen, Shervin Assassi, Chandra Mohan, Yasemin M. Akay

“…However, the need for a large training data set and the requirement for a graphics processing unit (GPU) have hindered the wide application of machine learning algorithms as a diagnostic tool in resource-constrained environments (e.g., clinics). …”
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Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning by Chenbo Yang, Chenbo Yang, Meichen Feng, Juan Bai, Hui Sun, Rutian Bi, Lifang Song, Chao Wang, Yu Zhao, Wude Yang, Lujie Xiao, Meijun Zhang, Xiaoyan Song

“…The 3 FOD calculation methods could all improve the correlation between the original spectral curve and Log(ChD) to varying degrees, but only the GL method and RL method could observe the change process of correlation with order changes, and the shorter the wavelength, the smaller the order, and the higher the correlation. …”
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Targeted inhibition of Gus-expressing Enterococcus faecalis to promote intestinal stem cell and epithelial renovation contributes to the relief of irinotecan chemotoxicity by dehyd... by Ruiyang Gao, Bei Yue, Cheng Lv, Xiaolong Geng, Zhilun Yu, Hao Wang, Beibei Zhang, Fangbin Ai, Ziyi Wang, Donghui Liu, Zhengtao Wang, Kaixian Chen, Wei Dou

“…The Partial Least Squares Path Model (PLS-PM) algorithm initially links E. faecalis to dysregulated epithelial renovation. …”
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Evaluating the Accuracy and Reliability of Real-World Digital Mobility Outcomes in Older Adults After Hip Fracture: Cross-Sectional Observational Study by Martin A Berge, Anisoara Paraschiv-Ionescu, Cameron Kirk, Arne Küderle, Encarna Micó-Amigo, Clemens Becker, Andrea Cereatti, Silvia Del Din, Monika Engdal, Judith Garcia-Aymerich, Karoline B Grønvik, Clint Hansen, Jeffrey M Hausdorff, Jorunn L Helbostad, Carl-Philipp Jansen, Lars Gunnar Johnsen, Jochen Klenk, Sarah Koch, Walter Maetzler, Dimitrios Megaritis, Arne Müller, Lynn Rochester, Lars Schwickert, Kristin Taraldsen, Beatrix Vereijken

“… BackgroundAlgorithms estimating real-world digital mobility outcomes (DMOs) are increasingly validated in healthy adults and various disease cohorts. …”
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Open-Source Data Logger System for Real-Time Monitoring and Fault Detection in Bench Testing by Marcio Luís Munhoz Amorim, Jorge Gomes Lima, Norah Nadia Sánchez Torres, Jose A. Afonso, Sérgio F. Lopes, João P. P. do Carmo, Lucas Vinicius Hartmann, Cicero Rocha Souto, Fabiano Salvadori, Oswaldo Hideo Ando Junior

“…Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines.…”
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A machine learning-based screening model for the early detection of prostate cancer developed using serum microRNA data from a mixed cohort of 8,741 participants by Cong Lai, Zhensheng Hu, Zhuohang Li, Zhikai Wu, Kuiqing Li, Lin Li, Hongze Liu, Juanyi Shi, Yi Zhou, Kewei Xu, Cheng Liu

“…Abstract Objective Early detection of prostate cancer (PCa) can improve the prognosis of patients. Currently, the role of the prostate specific antigen test for PCa screening remains debatable. …”
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