Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy by Haofang Zhang, Changbao Xu, Chenge Hu, Yunlai Xue, Daoke Yao, Yifan Hu, Ankang Wu, Miao Dai, Hang Ye

“…Our study aimed to construct a machine learning algorithm predictive model to predict the risk of fungal infection following F-URL. …”
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Prediction of pulmonary embolism by an explainable machine learning approach in the real world by Qiao Zhou, Ruichen Huang, Xingyu Xiong, Zongan Liang, Wei Zhang

“…To address this, we employed an artificial intelligence–based machine learning algorithm (MLA) to construct a robust predictive model for PE. …”
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AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening by Rui-Fang Lu, Chao-Yin She, Dan-Ni He, Mei-Qing Cheng, Ying Wang, Hui Huang, Ya-Dan Lin, Jia-Yi Lv, Si Qin, Ze-Zhi Liu, Zhi-Rong Lu, Wei-Ping Ke, Chao-Qun Li, Han Xiao, Zuo-Feng Xu, Guang-Jian Liu, Hong Yang, Jie Ren, Hai-Bo Wang, Ming-De Lu, Qing-Hua Huang, Li-Da Chen, Wei Wang, Ming Kuang

“…Abstract Hepatocellular carcinoma (HCC) ultrasound screening encounters challenges related to accuracy and the workload of radiologists. …”
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Machine learning to improve HIV screening using routine data in Kenya by Jonathan D. Friedman, Jonathan M. Mwangi, Kennedy J. Muthoka, Benedette A. Otieno, Jacob O. Odhiambo, Frederick O. Miruka, Lilly M. Nyagah, Pascal M. Mwele, Edmon O. Obat, Gonza O. Omoro, Margaret M. Ndisha, Davies O. Kimanga

“…We generated a stratified 60‐20‐20 train‐validate‐test split to assess model generalizability. We trained four machine learning algorithms including logistic regression, Random Forest, AdaBoost and XGBoost. …”
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GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach by Diego Bravo, Juan Frias, Felipe Vera, Juan Trejos, Carlos Martínez, Martín Gómez, Fabio González, Eduardo Romero

“…The dataset covers 22 anatomical landmarks in the stomach and includes an additional category for unqualified images, making it a valuable resource for AI model development. By providing a robust public dataset and baseline deep learning models for image and sequence classification, GastroHUN serves as a benchmark for future research and aids in the development of more effective algorithms.…”
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Deep Learning-Based Draw-a-Person Intelligence Quotient Screening by Shafaat Hussain, Toqeer Ehsan, Hassan Alhuzali, Ali Al-Laith

“…The primary objective of our research is to streamline the IQ screening process for psychologists by leveraging deep learning algorithms. …”
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Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA by Yang Sen, Wang Hengdi, Cui Yongcun, Li Chang, Tang Yuanchao

“…Finally, through the screening of coefficients of the variation method, the root mean square value and peak value are constructed as the two-dimensional eigenvalue vector of the first layer, and the sample entropy, kurtosis and root mean square are constructed as the three-dimensional eigenvalue vector of the second layer, which are respectively sent to the limit learning machine ELM for the training and classification of rolling bearing faults.The experiment results show that the proposed algorithm has good fault diagnosis performance,ultimately achieving a classification accuracy of 98.25% and an actual diagnostic accuracy of 93.36%.…”
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Development and Validation of a Machine Learning Algorithm for Predicting Diabetes Retinopathy in Patients With Type 2 Diabetes: Algorithm Development Study by Sunyoung Kim, Jaeyu Park, Yejun Son, Hojae Lee, Selin Woo, Myeongcheol Lee, Hayeon Lee, Hyunji Sang, Dong Keon Yon, Sang Youl Rhee

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A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD by Jinggeng Gao, Honglei Xu, Yong Yang, Haoming Niu, Jinping Liang, Haiying Dong

“…Finally, we use the EEMD algorithm to decompose the reconstructed signal; the intrinsic mode function (IMF) components containing wideband oscillation information are screened by the energy coefficient, and the wideband oscillation information is identified.…”
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Risk prediction of QTc prolongation occurrence in cancer patients treated with commonly used oral tyrosine kinase inhibitors: machine learning modeling or conventional statistical... by Hsiang-Wen Lin, Tien-Chao Lin, Chien-Ning Hsu, Tzu-Pei Yeh, Yu-Chieh Chen, Liang-Chih Liu, Chen-Yuan Lin

“…The backward LR method and seven ML algorithms were applied to train and test the prediction models. …”
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Immunogenic cell death genes in single-cell and transcriptome analyses perspectives from a prognostic model of cervical cancer by Li Ning, Li Ning, Xiu Li, Xiu Li, Yating Xu, Yating Xu, Yu Si, Yu Si, Hongting Zhao, Qinling Ren, Qinling Ren

“…This study sought to investigate the significance of ICD in CESC and to establish an ICDRs prognostic model to improve immunotherapy efficacy for patients with cervical cancer.MethodsICD-associated genes were screened at the single-cell and transcriptome levels based on AddModuleScore, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. …”
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A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities by Xueli Shen, Runyu Ma

“…To solve this problem, we propose a gradually accelerating environment adaptive consensus algorithm, AE-PBFT, that can be applied to IoV. It includes a trust management model that achieves gradual acceleration by recording the historical continuous behavior of nodes, thereby improving the efficiency of screening nodes with different intentions, accelerating the consensus process, and reducing latency. …”
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Carrier-independent screen-shooting resistant watermarking based on information overlay superimposition by Xiaomeng LI, Daidou GUO, Xunfang ZHUO, Heng YAO, Chuan QIN

“…Financial security, an important part of national security, is critical for the stable and healthy development of the economy.Digital image watermarking technology plays a crucial role in the field of financial information security, and the anti-screen watermarking algorithm has become a new research focus of digital image watermarking technology.The common way to achieve an invisible watermark in existing watermarking schemes is to modify the carrier image, which is not suitable for all types of images.To solve this problem, an end-to-end robust watermarking scheme based on deep learning was proposed.The algorithm achieved both visual quality and robustness of the watermark image.A random binary string served as the input of the encoder network in the proposed end-to-end network architecture.The encoder can generate the watermark information overlay, which can be attached to any carrier image after training.The ability to resist screen shooting noise was learned by the model through mathematical methods incorporated in the network to simulate the distortion generated during screen shooting.The visual quality of the watermark image was further improved by adding the image JND loss based on just perceptible difference.Moreover, an embedding hyperparameter was introduced in the training phase to balance the visual quality and robustness of the watermarked image adaptively.A watermark model suitable for different scenarios can be obtained by changing the size of the embedding hyperparameter.The visual quality and robustness performance of the proposed scheme and the current state-of-the-art algorithms were evaluated to verify the effectiveness of the proposed scheme.The results show that the watermark image generated by the proposed scheme has better visual quality and can accurately restore the embedded watermark information in robustness experiments under different distances, angles, lighting conditions, display devices, and shooting devices.…”
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Applicability of machine learning technique in the screening of patients with mild traumatic brain injury. by Miriam Leiko Terabe, Miyoko Massago, Pedro Henrique Iora, Thiago Augusto Hernandes Rocha, João Vitor Perez de Souza, Lily Huo, Mamoru Massago, Dalton Makoto Senda, Elisabete Mitiko Kobayashi, João Ricardo Vissoci, Catherine Ann Staton, Luciano de Andrade

“…Our predictive model can assist in the screening of mild TBI patients, assisting health professionals to manage the resource utilization, and improve the quality and safety of patient care.…”
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Catalyzing early ovarian cancer detection: Platelet RNA-based precision screening by Eunyong Ahn, Se Ik Kim, Sungmin Park, Sarah Kim, Hyejin Lee, Yeochan Kim, Sangick Park, Suyeon Lee, Dong Won Hwang, Heeyeon Kim, HyunA Jo, Untack Cho, Juwon Lee, Cheol Lee, TaeJin Ahn, Yong-Sang Song

“…We diverged from traditional methods by employing intron-spanning reads (ISR) counts rather than gene expression levels to use splice junctions as features in our models. If integrated with current screening methods, our algorithm holds promise for identifying ovarian or endometrial cancer in its early stages.…”
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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

“…This study aimed to explore the feasibility of utilising machine learning models to accurately screen for MASLD in large populations based on a combination of essential demographic and clinical characteristics. …”
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Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury by Tianyun Gao, Zhiqiang Nong, Yuzhen Luo, Manqiu Mo, Zhaoyan Chen, Zhenhua Yang, Ling Pan

“…Ensemble stepwise feature selection method was used to screen for effective features. The prediction models of short-term mortality were developed by seven machine learning algorithms. …”
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Artificial Intelligence in Virtual Screening: Transforming Drug Research and Discovery—A Review by Sayantani Roy, Karuppiah Nagaraj, Amit Mittal, Flora C. Shah, Kaliyaperumal Raja

“…Additionally, CHARMM software was applied for molecular dynamics simulations to calculate empirical energy functions. AI-driven algorithms such as KarmaDock and DeepDock were utilized for large-scale ligand screening and for improving protein–ligand docking accuracy. …”
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Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems. by Ali Raza, Akhtar Ali, Sami Ullah, Yasir Nadeem Anjum, Basit Rehman

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Measuring Optical Scattering in Relation to Coatings on Crystalline X-Ray Scintillator Screens by Matthias Diez, Simon Zabler

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