The Next Frontier in Brain Monitoring: A Comprehensive Look at In-Ear EEG Electrodes and Their Applications by Alexandra Stefania Mihai (Ungureanu), Oana Geman, Roxana Toderean, Lucas Miron, Sara SharghiLavan

“…Future research aims to improve device design for long-term monitoring, integrate advanced signal processing algorithms, and explore applications in neurorehabilitation and early diagnosis of neurodegenerative diseases.…”
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Deepfake Audio Detection for Urdu Language Using Deep Neural Networks by Omair Ahmad, Muhammad Sohail Khan, Salman Jan, Inayat Khan

“…Therefore, developing effective algorithms to distinguish fake audio from real audio is critical to preventing such frauds. …”
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RehabHand—A New Physical Rehabilitation Training Dataset: Construction and Benchmark Performances of the Relevant Hand Tasks by Sinh Huy Nguyen, Hoang Bach Nguyen, Thi Thu Hong Le, Chi Thanh Nguyen, Thi Thanh Huyen Nguyen, Quynh Tho Chu, Thi Lan Le, Thanh Hai Tran, Hai Vu

“…These labeled datasets are utilized to evaluate three primary tasks related to evaluating physical hand functions: including one- and two-stage neuronal networks for hand detection and identifying left or right patient’s hand; hand tracking with SORT and DeepSORT algorithms. We report a thorough performance analysis of the neuronal network models for the constructed dataset. …”
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White matter hyperintensity burden and infarct volume predict functional outcomes in anterior choroidal artery stroke: a multimodal MRI study by Weiwei Gao, Weiwei Gao, Weiwei Gao, Mingyang Wang, Jianzhong Lin, Junyi Huang, Lijuan Cai, Lijuan Cai, Xingyu Chen, Xingyu Chen, Renjing Zhu, Renjing Zhu, Renjing Zhu

“…WMH burden was assessed using the Fazekas visual rating scale and an automated volumetric quantification method based on lesion prediction algorithms. Acute infarct volume was precisely measured using fully automated threshold segmentation. …”
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Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma by Duo Wang, Duo Wang, Duo Wang, Jihao Tu, Jihao Tu, Jianfeng Liu, Jianfeng Liu, Yuting Piao, Yuting Piao, Yiming Zhao, Yiming Zhao, Ying Xiong, Ying Xiong, Jianing Wang, Jianing Wang, Xiaotian Zheng, Xiaotian Zheng, Bin Liu, Bin Liu

“…We developed a novel machine learning framework that incorporated 12 machine learning algorithms and their 127 combinations to construct a consensus GPRS to screen biomarkers with diagnostic significance and clinical translation, which was assessed by the internal and external validation datasets. …”
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Dynamic selectout and voting-based federated learning for enhanced medical image analysis by Saeed Iqbal, Adnan N Qureshi, Musaed Alhussein, Khursheed Aurangzeb, Atif Mahmood, Saaidal Razalli Bin Azzuhri

“…The voting system and the dynamic SelectOut algorithms improve the convergence of the FL model and successfully handle the difficulties presented by uneven and heterogeneous datasets. …”
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Exploring ribosome biogenesis in lung adenocarcinoma to advance prognostic methods and immunotherapy strategies by Zipei Song, Yuheng Wang, Miaolin Zhu, Pengpeng Zhang, Zhihua Li, Xin Geng, Xincen Cao, Jianan Zheng, Jianwei Tang, Liang Chen

“…Employing various machine learning algorithms, a ribosome biogenesis-related signature (RBS) was constructed and compared to 140 published LUAD prognostic models. …”
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A multidimensional assessment of adverse events associated with paliperidone palmitate: a real-world pharmacovigilance study using the FAERS and JADER databases by Siyu Lou, Zhiwei Cui, Yingyong Ou, Junyou Chen, Linmei Zhou, Ruizhen Zhao, Chengyu Zhu, Li Wang, Zhu Wu, Fan Zou

“…Results A total of 27,672 ADE reports related to paliperidone palmitate were identified in FAERS, with 285 significantly disproportionate preferred terms (PTs) identified by all four algorithms. Paliperidone palmitate-associated ADEs encompassed 27 System Organ Classes (SOCs). …”
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Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks by Hongna Tan, Qingxia Wu, Yaping Wu, Bingjie Zheng, Bo Wang, Yan Chen, Lijuan Du, Jing Zhou, Fangfang Fu, Huihui Guo, Cong Fu, Lun Ma, Pei Dong, Zhong Xue, Dinggang Shen, Meiyun Wang

“…Critical relevance statement An AI risk assessment tool employing deep learning algorithms was developed and validated for enhancing breast cancer diagnosis from mammograms, to improve risk stratification accuracy, particularly in patients with dense breasts, and serve as a decision support aid for radiologists. …”
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CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study by Yongjie Zhou, Yuan Zhan, Jinhong Zhao, Linhua Zhong, Fei Zou, Xuechao Zhu, Qiao Zeng, Jiayu Nan, Lianggeng Gong, Yongming Tan, Lan Liu

“…Radiomic features were extracted from CT venous phase images and modeled using six machine learning algorithms. The maximum 2D sections and 3D images of each tumor were input into four ResNet models to obtain predictive probabilities. …”
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Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review by Umile Giuseppe Longo, Alberto Lalli, Guido Nicodemi, Matteo Giuseppe Pisani, Alessandro De Sire, Pieter D'Hooghe, Ara Nazarian, Jacob F. Oeding, Balint Zsidai, Kristian Samuelsson

“…Studies with insufficient data regarding the output variable used to assess the reliability of the ML model, those applying deterministic algorithms, unrelated topics, protocol studies, and other systematic reviews were excluded from the final synthesis. …”
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Opportunistic Diagnostics of Dental Implants in Routine Clinical Photon-Counting CT Acquisitions by Maurice Ruetters, Holger Gehrig, Christian Mertens, Sinan Sen, Ti-Sun Kim, Heinz-Peter Schlemmer, Christian H. Ziener, Stefan Schoenberg, Matthias Froelich, Marc Kachelrieß, Stefan Sawall

“…This study evaluates the diagnostic utility of PCCT for visualizing peri-implant structures in routine clinical photon-counting CT acquisitions and assesses the influence of metal artifact reduction (MAR) algorithms on image quality. Ten dental implants were retrospectively included in this IRB-approved study. …”
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Exploring the role of ferroptosis in pemphigus: identification of diagnostic markers and regulatory mechanisms by Jing Mao, Jianping Lan, Zheyu Zhuang, Ying Chen, Ying Chen, Yushan Ou, Xinhong Su, Xueting Zeng, Fuchen Huang, Zequn Tong, Xiaoqing Lv, Xiaoqing Lv, Xiaoqing Lv, Hui Ke, Zhenlan Wu, Ying Zou, Bo Cheng, Bo Cheng, Bo Cheng, Chao Ji, Chao Ji, Chao Ji, Ting Gong

“…Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify co-expressed gene modules related to pemphigus. Machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were used to select key ferroptosis-related genes. …”
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Multiple loci are associated with white blood cell phenotypes. by Michael A Nalls, David J Couper, Toshiko Tanaka, Frank J A van Rooij, Ming-Huei Chen, Albert V Smith, Daniela Toniolo, Neil A Zakai, Qiong Yang, Andreas Greinacher, Andrew R Wood, Melissa Garcia, Paolo Gasparini, Yongmei Liu, Thomas Lumley, Aaron R Folsom, Alex P Reiner, Christian Gieger, Vasiliki Lagou, Janine F Felix, Henry Völzke, Natalia A Gouskova, Alessandro Biffi, Angela Döring, Uwe Völker, Sean Chong, Kerri L Wiggins, Augusto Rendon, Abbas Dehghan, Matt Moore, Kent Taylor, James G Wilson, Guillaume Lettre, Albert Hofman, Joshua C Bis, Nicola Pirastu, Caroline S Fox, Christa Meisinger, Jennifer Sambrook, Sampath Arepalli, Matthias Nauck, Holger Prokisch, Jonathan Stephens, Nicole L Glazer, L Adrienne Cupples, Yukinori Okada, Atsushi Takahashi, Yoichiro Kamatani, Koichi Matsuda, Tatsuhiko Tsunoda, Toshihiro Tanaka, Michiaki Kubo, Yusuke Nakamura, Kazuhiko Yamamoto, Naoyuki Kamatani, Michael Stumvoll, Anke Tönjes, Inga Prokopenko, Thomas Illig, Kushang V Patel, Stephen F Garner, Brigitte Kuhnel, Massimo Mangino, Ben A Oostra, Swee Lay Thein, Josef Coresh, H-Erich Wichmann, Stephan Menzel, JingPing Lin, Giorgio Pistis, André G Uitterlinden, Tim D Spector, Alexander Teumer, Gudny Eiriksdottir, Vilmundur Gudnason, Stefania Bandinelli, Timothy M Frayling, Aravinda Chakravarti, Cornelia M van Duijn, David Melzer, Willem H Ouwehand, Daniel Levy, Eric Boerwinkle, Andrew B Singleton, Dena G Hernandez, Dan L Longo, Nicole Soranzo, Jacqueline C M Witteman, Bruce M Psaty, Luigi Ferrucci, Tamara B Harris, Christopher J O'Donnell, Santhi K Ganesh

“…We implemented gene-clustering algorithms to evaluate functional connectivity among implicated loci and showed functional relationships across cell types. …”
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Identification of biomarkers and immune microenvironment associated with pterygium through bioinformatics and machine learning by Li-Wei Zhang, Ji Yang, Hua-Wei Jiang, Hua-Wei Jiang, Xiu-Qiang Yang, Ya-Nan Chen, Wei-Dang Ying, Ying-Liang Deng, Min-hui Zhang, Hai Liu, Hong-Lei Zhang

“…Additionally, we utilized weighted correlation network analysis (WGCNA) to select module genes and applied Random Forest (RF) and Support Vector Machine (SVM) algorithms to identify pivotal feature genes influencing pterygium progression. …”
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Oxidative balance score predicts chronic kidney disease risk in overweight adults: a NHANES-based machine learning study by Leying Zhao, Leying Zhao, Cong Zhao, Cong Zhao, Yuchen Fu, Yuchen Fu, Xiaochang Wu, Xiaochang Wu, Xuezhe Wang, Xuezhe Wang, Yaoxian Wang, Yaoxian Wang, Yaoxian Wang, Huijuan Zheng

“…Additionally, 14 machine learning algorithms were trained and validated using SMOTE-balanced data and five-fold cross-validation. …”
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Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients by Olawande Daramola, Tatenda Duncan Kavu, Maritha J. Kotze, Jeanine L. Marnewick, Oluwafemi A. Sarumi, Boniface Kabaso, Thomas Moser, Karl Stroetmann, Isaac Fwemba, Fisayo Daramola, Martha Nyirenda, Susan J. van Rensburg, Peter S. Nyasulu

“…This study aimed to investigate the performance and interpretability of several ML algorithms, including deep multilayer perceptron (Deep MLP), support vector machine (SVM) and Extreme gradient boosting trees (XGBoost) for predicting COVID-19 mortality risk with an emphasis on the effect of cross-validation (CV) and principal component analysis (PCA) on the results. …”
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Thrombocytopenia in patients with melanoma receiving immune checkpoint inhibitor therapy by Michael Postow, Igor Puzanov, Suthee Rapisuwon, Michael A. Davies, Zeynep Eroglu, Eileen Shiuan, Kathryn E. Beckermann, Alpaslan Ozgun, Ciara Kelly, Meredith McKean, Jennifer McQuade, Mary Ann Thompson, John P. Greer, Douglas Johnson

“…In a retrospective chart review of 2360 patients with melanoma treated with checkpoint inhibitor therapy, <1% experienced thrombocytopenia following immune checkpoint inhibition, and of these, most had spontaneous resolution and did not require treatment.Conclusions Thrombocytopenia, especially ITP, induced by immune checkpoint inhibitors appears to be an uncommon irAE that is manageable with observation in mild cases and/or standard ITP treatment algorithms. In our series, the majority of patients had mild thrombocytopenia that resolved spontaneously or responded to standard corticosteroid regimens. …”
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Identificación y Control Wavenet de un Motor de CA by L. E. Ramos Velasco, J. C. Ramos Fernández, O. Islas Gómez, J. García Lamont, M.A. Espejel Rivera, M.A. Márquez Vera

“…Palabras clave: Control de motores, Controlador PID, Redes neuronales wavelets, Algoritmos auto-ajustables, Keywords: Motor control, PID controller, Wavelet neural networks, Selfadaptive algorithms.…”
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Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review by Claire R. van Genugten, Claire R. van Genugten, Melissa S. Y. Thong, Melissa S. Y. Thong, Melissa S. Y. Thong, Wouter van Ballegooijen, Wouter van Ballegooijen, Wouter van Ballegooijen, Annet M. Kleiboer, Annet M. Kleiboer, Donna Spruijt-Metz, Arnout C. Smit, Mirjam A. G. Sprangers, Mirjam A. G. Sprangers, Yannik Terhorst, Yannik Terhorst, Heleen Riper, Heleen Riper, Heleen Riper

“…To accomplish this, JITAIs often apply complex analytic techniques, such as machine learning or Bayesian algorithms to real- or near-time data acquired from smartphones and other sensors. …”
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