Two-Step Screening for Depression and Anxiety in Patients with Cancer: A Retrospective Validation Study Using Real-World Data by Bryan Gascon, Joel Elman, Alyssa Macedo, Yvonne Leung, Gary Rodin, Madeline Li

“…<b>Conclusions:</b> The present study is among the first to demonstrate that a two-step screening algorithm for depression may improve depression screening in cancer using real-world data. …”
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Pupil light reflex evoked by light-emitting diode and computer screen: Methodology and association with need for recovery in daily life. by Yang Wang, Adriana A Zekveld, Dorothea Wendt, Thomas Lunner, Graham Naylor, Sophia E Kramer

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Evaluating the generalisability of region-naïve machine learning algorithms for the identification of epilepsy in low-resource settings. by Ioana Duta, Symon M Kariuki, Anthony K Ngugi, Angelina Kakooza Mwesige, Honorati Masanja, Daniel M Mwanga, Seth Owusu-Agyei, Ryan Wagner, J Helen Cross, Josemir W Sander, Charles R Newton, Arjune Sen, Gabriel Davis Jones

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Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes. by Nishanthi Periyathambi, Durga Parkhi, Yonas Ghebremichael-Weldeselassie, Vinod Patel, Nithya Sukumar, Rahul Siddharthan, Leelavati Narlikar, Ponnusamy Saravanan

“…<h4>Objective</h4>The aim of the present study was to identify the factors associated with non-attendance of immediate postpartum glucose test using a machine learning algorithm following gestational diabetes mellitus (GDM) pregnancy.…”
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Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets by Catarina Lopes, Andreia Brandão, Manuel R. Teixeira, Mário Dinis-Ribeiro, Carina Pereira

“…Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. …”
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Cervical cancer demystified: exploring epidemiology, risk factors, screening, treatment modalities, preventive measures, and the role of artificial intelligence by N. Mohammad, M. Khan, M. Maqsood, A. H. K. Naseeb

“…However, disparities persist due to limited healthcare infrastructure and access to routine screening. AI-driven technologies, including deep learning algorithms and machine learning models, are emerging as valuable tools in cervical cancer detection, risk assessment, and treatment planning. …”
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Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network by Xingyu Duan, Xiaojun Ma, Mengqi Zhu, Linan Wang, Dingqi You, Lili Deng, Ningkui Niu

“…We have developed a deep learning-based image segmentation model to enhance the efficiency of scoliosis screening. …”
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Comparison of Deep Learning and Clinician Performance for Detecting Referable Glaucoma from Fundus Photographs in a Safety Net Population by Van Nguyen, MD, Sreenidhi Iyengar, Haroon Rasheed, MD, Galo Apolo, Zhiwei Li, Aniket Kumar, Hong Nguyen, Austin Bohner, MD, Kyle Bolo, MD, Rahul Dhodapkar, MD, Jiun Do, MD, PhD, Andrew T. Duong, MD, Jeffrey Gluckstein, MD, Kendra Hong, MD, Lucas L. Humayun, Alanna James, MD, Junhui Lee, MD, Kent Nguyen, OD, Brandon J. Wong, MD, Jose-Luis Ambite, PhD, Carl Kesselman, PhD, Lauren P. Daskivich, MD, Michael Pazzani, PhD, Benjamin Y. Xu, MD, PhD

“…Implementation of this algorithm in screening workflows could help reallocate resources and provide more reproducible and timely glaucoma care. …”
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Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis by Jingtai Ma, Jingtai Ma, Yiting Fang, Yiting Fang, Shiqi Li, Shiqi Li, Lilian Zeng, Siyi Chen, Zhifeng Li, Guiyuan Ji, Xingfen Yang, Wei Wu, Wei Wu

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Identifying Molecular Properties of Ataxin-2 Inhibitors for Spinocerebellar Ataxia Type 2 Utilizing High-Throughput Screening and Machine Learning by Smita Sahay, Jingran Wen, Daniel R. Scoles, Anton Simeonov, Thomas S. Dexheimer, Ajit Jadhav, Stephen C. Kales, Hongmao Sun, Stefan M. Pulst, Julio C. Facelli, David E. Jones

“…The molecular descriptor data (MD model) was analyzed separately from the experimentally determined screening data (S model) as well as together (MD-S model). …”
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Artificial Intelligence With Neural Network Algorithms in Pediatric Astrocytoma Diagnosis: A Systematic Review by Floresya K. Farmawati, Della W.A. Nurwakhid, Tifani A. Pradhea, Rayyan Fitriasa, Hutami H. Arrahmi, Muhana F. Ilyas, Fadhilah T. Nur

“…The AI models exhibited performance levels comparable to or exceeding that of expert radiologists, with metrics such as tumor classification accuracy of 92% and high values of the area under the receiver operating characteristic curve.Conclusions: AI with neural network algorithms shows significant promise in enhancing accuracy of pediatric astrocytoma diagnosis. …”
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Surrogate-assisted global and distributed local collaborative optimization algorithm for expensive constrained optimization problems by Xiangyong Liu, Zan Yang, Jiansheng Liu, Junxing Xiong, Jihui Huang, Shuiyuan Huang, Xuedong Fu

“…For global surrogate-assisted collaborative evolution phase, the global candidate set is generated through classification collaborative mutation operations to alleviate the pre-screening pressure of the surrogate model. For local surrogate-assisted phase, a distributed central region local exploration is designed to achieve intensively search for promising distributed local areas which are located by affinity propagation clustering and mathematical modeling. …”
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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm by Jing Yang, Touseef Sadiq, Jiale Xiong, Muhammad Awais, Uzair Aslam Bhatti, Roohallah Alizadehsani, Juan Manuel Gorriz

“…To overcome these challenges, the approach proposed incorporates advanced techniques such as convolutional neural networks (CNNs), an improved differential evolution (DE) algorithm for pre‐training, and a reinforcement learning (RL)‐based model for training. …”
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Optimizing protein-ligand docking through machine learning: algorithm selection with AutoDock Vina by Ala’ Omar Hasan Zayed

“…Abstract Context Understanding protein-ligand interactions is fundamental to drug design, where optimizing docking parameter selection can potentially enhance computational efficiency and resource allocation in virtual screening. While numerous algorithms exist for protein-ligand docking, achieving an optimal balance between accuracy and computational speed remains challenging. …”
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An Automated Algorithm for Obstructive Sleep Apnea Detection Using a Wireless Abdomen-Worn Sensor by Thi Hang Dang, Seong-mun Kim, Min-seong Choi, Sung-nam Hwan, Hyung-ki Min, Franklin Bien

“…Wireless wearable devices have emerged as promising tools for OSA screening and follow-up. This study introduces a novel automated algorithm for detecting OSA using abdominal movement signals and acceleration data collected by a wireless abdomen-worn sensor (Soomirang). …”
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Digital innovation in healthcare: quantifying the impact of digital sepsis screening tools on patient outcomes—a multi-site natural experiment by Sarah Tonkin-Crine, Graham Cooke, Anthony C Gordon, Anthony Gordon, Shashank Patil, Runa Lazzarino, Andrew Brent, Kate Honeyford, Céire E Costelloe, Anne Kinderlerer, John Welch, Peter Ghazal, Claire Burnett, Alf Timney, Andrew Jonathan Brent, Cerie Costelloe, Pippa Goodman Ben Glampson

“…We evaluated the impact of sepsis screening tools on in-patient 30-day mortality across four multi-hospital NHS Trusts, each using a different algorithm for early detection of sepsis.Methods Using quasi-experimental methods, we investigated the impact of the screening tools. …”
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Data Mining and Analysis of the Compatibility Law of Traditional Chinese Medicines Based on FP-Growth Algorithm by Shuchun Zhou

“…In terms of compatibility law of traditional Chinese antiviral prescriptions, this paper studied the compatibility law of traditional Chinese antiviral prescriptions based on the FP-growth algorithm and made exploratory research on the compatibility law information of 961 traditional classical antiviral prescriptions. …”
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One scan, multiple insights: A review of AI-Driven biomarker imaging and composite measure detection in lung cancer screening by Saher Verma, Leander Maerkisch, Alberto Paderno, Leonard Gilberg, Bianca Teodorescu, Mathias Meyer

“…Through an extensive review of current literature sourced from PubMed, the review highlights advancements in AI-driven biomarker detection, evaluates the potential benefits of a broader diagnostic approach, and addresses challenges related to model standardization and clinical integration. AI-enhanced LDCT screening shows significant promise in augmenting routine screenings, potentially advancing early detection, comprehensive patient assessments, and overall disease management across multiple health conditions.…”
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Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production by Kristen L. Beck, Niina Haiminen, Akshay Agarwal, Anna Paola Carrieri, Matthew Madgwick, Jennifer Kelly, Victor Pylro, Ban Kawas, Martin Wiedmann, Erika Ganda

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Ensemble Algorithm Based on Gene Selection, Data Augmentation, and Boosting Approaches for Ovarian Cancer Classification by Zne-Jung Lee, Jing-Xun Cai, Liang-Hung Wang, Ming-Ren Yang

“…Data augmentation allows researchers to expand the dataset, providing a larger and more diverse set of examples for model training. …”
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