Artificial Intelligence in Identifying Patients With Undiagnosed Nonalcoholic Steatohepatitis by Onur Baser, Gabriela Samayoa, Nehir Yapar, Erdem Baser

“…We performed a claims data analysis using a machine learning algorithm. To build our model, the study population was randomly divided into an 80% training subset and a 20% testing subset and tested and trained using a cross-validation technique. …”
Get full text

Road Perception for Autonomous Driving: Pothole Detection in Complex Environments Based on Improved YOLOv8 by Siyuan Kong, Qiao Meng, Xin Li, Zhijie Wang, Xin Liu, Bingyu Li

“…To address this, this paper proposes an innovative improved algorithm, which is based on the YOLOv8 model, and introduces the MSF-HFEB module in the innovative design. …”
Get full text

Increasing comprehensiveness and reducing workload in a systematic review of complex interventions using automated machine learning by Olalekan A Uthman, Rachel Court, Jodie Enderby, Lena Al-Khudairy, Chidozie Nduka, Hema Mistry, GJ Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…Background As part of our ongoing systematic review of complex interventions for the primary prevention of cardiovascular diseases, we have developed and evaluated automated machine-learning classifiers for title and abstract screening. The aim was to develop a high-performing algorithm comparable to human screening. …”
Get full text

Wavelet Transform-Based 3D Landscape Design and Optimization for Digital Cities by Yang Chen, Xiaolin Wang, Chang Zhang

“…The algorithm extracts mixed feature information of local long path and local short path based on the information retention module, and decomposes the information by combining wavelet transform, inputs the different components obtained from the decomposition into the network for training, and removes the noise by subsequent feature screening of the network structure. …”
Get full text

A comparison of tools used for tuberculosis diagnosis in resource-limited settings: a case study at Mubende referral hospital, Uganda. by Adrian Muwonge, Sydney Malama, Barend M de C Bronsvoort, Demelash Biffa, Willy Ssengooba, Eystein Skjerve

“…The three-predictor screening algorithm with and without DZM classified 50% and 33% of the true cases respectively, while the adjusted algorithm with DZM classified 78% of the true cases.…”
Get full text

A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study by Aoyu Li, Jingwen Li, Yishan Hu, Yan Geng, Yan Qiang, Juanjuan Zhao

“…To address the challenges (eg, the curse of dimensionality and increased model complexity) posed by high-dimensional features, we developed a dynamic adaptive feature selection optimization algorithm to identify the most impactful subset of features for classification performance. …”
Get full text

The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression by Chunxiao Zhang, Junjie Yue

“…At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.…”
Get full text

Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology. by Yuxi Long, Bruce R Donald

“…Compared to current binding affinity prediction algorithms, PATH+ shows similar or better accuracy and is more generalizable across orthogonal datasets. …”
Get full text

Breast cancer detection and classification with digital breast tomosynthesis: a two-stage deep learning approach by Yazeed Alashban

“…CLINICAL SIGNIFICANCE: The proposed two-tier DL algorithm, combining a modified VGG19 model for image classification and YOLOv5-CBAM for lesion detection, can improve the accuracy, efficiency, and reliability of breast cancer screening and diagnosis through innovative artificial intelligence-driven methodologies.…”
Get full text

Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy. by Lin Lin, Elena Dacal, Nuria Díez, Claudia Carmona, Alexandra Martin Ramirez, Lourdes Barón Argos, David Bermejo-Peláez, Carla Caballero, Daniel Cuadrado, Oscar Darias-Plasencia, Jaime García-Villena, Alexander Bakardjiev, Maria Postigo, Ethan Recalde-Jaramillo, Maria Flores-Chavez, Andrés Santos, María Jesús Ledesma-Carbayo, José M Rubio, Miguel Luengo-Oroz

“…It achieved an overall precision of 94.14%, recall of 91.90% and F1 score of 93.01% for the screening algorithm and 95.46%, 97.81% and 96.62% for the species differentiation algorithm respectively. …”
Get full text

A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs by Pengyu Chu, Bo Han, Qiang Guo, Yiping Wan, Jingjing Zhang

“…Experimental data show that, in the task of organ segmentation throughout the entire cotton growth cycle, the ResDGCNN model achieved a segmentation accuracy of 67.55%, with a 4.86% improvement in mIoU compared to the baseline model. …”
Get full text

RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification by Yutong Wang, Ziming Kou, Cong Han, Yuchen Qin

“…Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. …”
Get full text

Comparative performance of deep learning architectures for diabetic peripheral neuropathy detection using corneal confocal microscopy: a retrospective single-centre study by Yuyang Deng, Wenqu Chen, Weihuang Xu, Jianzhang Hu

“…For single-image predictions in the three-class classification task of CCM images, the InceptionV3 model achieved a precision of 0.8385, a recall of 0.9083, an F1 score of 0.8720 and an AUC of 0.8769 for predicting DPN+.Conclusions The InceptionV3-based DLA model achieved superior performance compared with traditional convolutional neural network architectures like ResNet and DenseNet, and the Swin transformer model, highlighting its potential for effective DPN screening.…”
Get full text

Naive Bayes Analysis for Nutritional Fulfillment Prediction in Children by Satrio Agung Wicaksono, Satrio Hadi Wijoyo, Fatmawati Fatmawati, Tri Afirianto, Diva Kurnianingtyas, Mochammad Chandra Saputra

“…The study’s implications are twofold: practically, the model can be integrated into health monitoring systems to assist healthcare professionals and policymakers in designing more effective nutrition programs; theoretically, it highlights the adaptability of Naive Bayes for handling complex, multi-dimensional health data. …”
Get full text

A Fuzzy Supplier Selection Application Using Large Survey Datasets of Delivery Performance by Jonathan Davis, Margaret F. Shipley, Gary Stading

“…A model is developed using fuzzy probability to screen survey data across relevant criteria for selecting suppliers based on fuzzy expected values. …”
Get full text

Application of artificial intelligence in the diagnosis and treatment of lacrimal disorders: challenges and opportunities by PENG Xintong, LI Guangyu

“…AI has the ability to provide more precise disease identification and treatment strategies through efficient image analysis, multimodal data fusion, and deep learning algorithms. …”
Get full text

The application of compressed sensing on tumor mutation burden calculation from overlapped pooling sequencing data by Yue Cui, Yi Qiao, Rongming An, Xuan Pan, Jing Tu

“…Additionally, we performed an assessment of the reconstruction efficiency of both the BP model and the OMP model.…”
Get full text

Mitochondrial-Localized Protein Transcript Abundance can Predict the Prognosis of Endometrial Carcinoma: A Retrospective Analysis by Weifei Liang, Hong Lin, Donglin Sun, Minghui Tang

“…Moreover, a nomogram was constructed through the combination of the scoring algorithm and the patient’s clinical features. Conclusions: The scoring algorithm based on mitochondrial gene expression can assist clinicians in predicting the postoperative survival rate of patients, allowing them to devise more precise treatment programs.…”
Get full text

Perceived age estimation from facial image and demographic data in young and middle-aged South Korean adults by Ilkoo Ahn, Younghwa Baek, Bok-Nam Seo, Su Eun Lim, Kyoungsik Jung, Ho Seok Kim, Jeongkyun Kim, Sukyung Lee, Siwoo Lee

“…The averaging models of Lasso, XGBoost, and CatBoost showed a mean absolute error of 2.2944, indicating that this algorithm can be used as a screening method for general health status in the population.…”
Get full text

Implementation costs and cost-effectiveness of ultraportable chest X-ray with artificial intelligence in active case finding for tuberculosis in Nigeria. by Tushar Garg, Stephen John, Suraj Abdulkarim, Adamu D Ahmed, Beatrice Kirubi, Md Toufiq Rahman, Emperor Ubochioma, Jacob Creswell

“…We provide implementation cost and cost-effectiveness estimates of different screening algorithms using symptoms, CXR and AI in Nigeria. …”
Get full text