Artificial intelligence technology in ophthalmology public health: current applications and future directions by ShuYuan Chen, Wen Bai, Wen Bai

“…Key issues include interoperability with electronic health records (EHR), data security and privacy, data quality and bias, algorithm transparency, and ethical and regulatory frameworks. …”
Get full text

Advancements in Machine Learning (ML): Transforming the Future of Blood Cancer Detection and Outcome Prediction by Wiebke Rösler, Michael Roiss, Corinne Widmer

“…The diagnosis and treatment of hematologic malignancies are becoming more and more complex. Growing knowledge of pathophysiology, diagnostic methods and, last but not least, treatment options offer many opportunities for patients, but integrating the growing amount of knowledge into daily practice can be challenging. …”
Get full text

Advancements in the application of artificial intelligence in the field of colorectal cancer by Mengying Zhu, Mengying Zhu, Zhenzhu Zhai, Yue Wang, Fang Chen, Ruibin Liu, Ruibin Liu, Xiaoquan Yang, Guohua Zhao

“…In this context, artificial intelligence (AI) has shown immense potential in revolutionizing CRC management, serving as one of the most effective screening tools. AI, utilizing machine learning (ML) and deep learning (DL) algorithms, improves early detection, diagnosis, and treatment by processing large volumes of medical data, uncovering hidden patterns, and forecasting disease development. …”
Get full text

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study by Zhen Lu, Binhua Dong, Hongning Cai, Tian Tian, Junfeng Wang, Leiwen Fu, Bingyi Wang, Weijie Zhang, Shaomei Lin, Xunyuan Tuo, Juntao Wang, Tianjie Yang, Xinxin Huang, Zheng Zheng, Huifeng Xue, Shuxia Xu, Siyang Liu, Pengming Sun, Huachun Zou

“…We trained a supervised machine learning model and developed pathways to classify individuals before evaluating its diagnostic validity and usability on an external cohort. …”
Get full text

Bioinformatics analysis of ferroptosis-related biomarkers and potential drug predictions in doxorubicin-induced cardiotoxicity by Jian Yu, Jian Yu, Jiangtao Wang, Xinya Liu, Xinya Liu, Cancan Wang, Cancan Wang, Li Wu, Yuanming Zhang, Yuanming Zhang

“…Utilized LASSO regression, SVM-RFE, and RF algorithms to identify key genes, followed by validation using external datasets (GSE232331, GSE230638) and ROC curve plotting to determine the diagnostic value of key genes. …”
Get full text

Estimation of potato leaf area index based on spectral information and Haralick textures from UAV hyperspectral images by Jiejie Fan, Jiejie Fan, Yang Liu, Yang Liu, Yiguang Fan, Yihan Yao, Riqiang Chen, Mingbo Bian, Yanpeng Ma, Huifang Wang, Haikuan Feng, Haikuan Feng, Haikuan Feng

“…Three types of spectral data—original spectral reflectance (OSR), first-order differential spectral reflectance (FDSR), and vegetation indices (VIs)—along with three types of Haralick textures—simple, advanced, and higher-order—were analyzed for their correlation with LAI across multiple growth stages. A model for LAI estimation in potato at multiple growth stages based on spectral and textural features screened by the successive projection algorithm (SPA) was constructed using partial least squares regression (PLSR), random forest regression (RFR) and gaussian process regression (GPR) machine learning methods. …”
Get full text

Accuracy and interpretability of smartwatch electrocardiogram for early detection of atrial fibrillation: A systematic review and meta‐analysis by Dr. Muhammad Iqhrammullah, Prof. Asnawi Abdullah, Dr. Hermansyah, Fahmi Ichwansyah, Prof. Dr. Ir. Hafnidar A. Rani, Meulu Alina, Artha M. T. Simanjuntak, Derren D. C. H. Rampengan, dr. Seba Talat Al‐Gunaid, dr. Naufal Gusti, dr. Arditya Damarkusuma, Edza Aria Wikurendra

“…Methods Data derived from indexed literature in the Scopus, Scilit, PubMed, Google Scholar, Web of Science, IEEE, and Cochrane Library databases (as of June 1, 2024) were systematically screened and extracted. The quantitative synthesis was performed using a two‐level mixed‐effects logistic regression model, as well as a proportional analysis with Freeman‐Tukey double transformation on a restricted maximum‐likelihood model. …”
Get full text

Geographic variation in secondary metabolites contents and their relationship with soil mineral elements in Pleuropterus multiflorum Thunb. from different regions by Yaling Yang, Siman Wang, Ruibin Bai, Feng Xiong, Yan Jin, Hanwei Liu, Ziyi Wang, Chengyuan Yang, Yi Yu, Apu Chowdhury, Chuanzhi Kang, Jian Yang, Lanping Guo

“…Conversely, a positive correlation was found between the contents of elements Na, Ce, Ti, and physcion and THSG-5, 2 components that exhibited higher levels in Deqing. Furthermore, an RF algorithm was employed to establish an interrelationship model, effectively forecasting the abundance of the majority of differential metabolites in HSW samples based on the content data of soil mineral elements. …”
Get full text

Prevalence estimates of trafficking in persons using statistical definitions: a cross-sectional high-risk community survey in Cape Town, South Africa by Rumi Kato Price, Annah K Bender, Floriana H Milazzo, Edna G. Rich, Nicolette V. Roman, Sheldon X Zhang, Erica L Koegler

“…Secondary outcome measures included individual and summary measures from the two screeners.Results Our PRIF algorithm yielded a TIP lifetime prevalence rate of 17.0% and past 12-month rate of 2.9%. …”
Get full text

Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population. by John T Murchison, Gillian Ritchie, David Senyszak, Jeroen H Nijwening, Gerben van Veenendaal, Joris Wakkie, Edwin J R van Beek

“…<h4>Objective</h4>In this study, we evaluated a commercially available computer assisted diagnosis system (CAD). The deep learning algorithm of the CAD was trained with a lung cancer screening cohort and developed for detection, classification, quantification, and growth of actionable pulmonary nodules on chest CT scans. …”
Get full text

Nitrogen content estimation of apple trees based on simulated satellite remote sensing data by Meixuan Li, Xicun Zhu, Xicun Zhu, Xinyang Yu, Cheng Li, Dongyun Xu, Ling Wang, Dong Lv, Yuyang Ma

“…Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN) algorithms were used to construct and screen the optimal models for apple tree nitrogen content estimation.ResultsResults showed that visible light, red edge, near-infrared, and yellow edge bands were sensitive bands for estimating apple tree nitrogen content. …”
Get full text

RAD-Seq-derived SSR markers: a new paradigm for genetic analysis and construction of genetically improved production populations in Pinus koraiensis by Pingyu Yan, Wanying Zhang, Junfei Hao, Xiaotian Miao, Jun Wu, Zixiong Xie, Zhixin Li, Lei Zhang, Hanguo Zhang

“…A production population of 20 individuals was constructed via the simulated annealing algorithm, which exhibited a more reasonable mating system (F=−0.028) and demonstrated superior cone production compared with that of the plus tree population, with an increase of 79.6%. …”
Get full text

Machine learning for clustering and classification of early knee osteoarthritis using single-leg standing kinematics by Ui-Jae Hwang, Kyu Sung Chung, Sung-Min Ha

“…This study investigated the application of machine learning techniques to single-leg standing (SLS) kinematics to classify and predict EOA. (1) To identify distinct groups based on SLS kinematic patterns using unsupervised learning algorithms, (2) to develop supervised learning models to predict EOA status, and (3) to identify the most influential kinematic variables associated with EOA. …”
Get full text

Combining Near-Infrared Spectroscopy and Chemometrics for Rapid Recognition of an Hg-Contaminated Plant by Bang-Cheng Tang, Hai-Yan Fu, Qiao-Bo Yin, Zeng-Yan Zhou, Wei Shi, Lu Xu, Yuan-Bin She

“…The NIRS measurements of impacted sample powders were collected in the mode of reflectance. The DUPLEX algorithm was utilized to split the NIRS data into representative training and test sets. …”
Get full text

Artificial Intelligence: A Review of Objective Grading and Quantification of Posterior Capsular Opacification by Saurabh Kushwaha, Rajat Chaudhary, Uma Devi

“…Here, we systematically reviewed several PCO imaging modalities, various existing AI algorithms, steps in building AI models and matrix evaluation in AI diagnosis of PCO. …”
Get full text

Machine learning approach effectively discriminates between Parkinson’s disease and progressive supranuclear palsy: Multi-level indices of rs-fMRI by Weiling Cheng, Xiao Liang, Wei Zeng, Jiali Guo, Zhibiao Yin, Jiankun Dai, Daojun Hong, Fuqing Zhou, Fangjun Li, Xin Fang

“…Various rs-fMRI indices were extracted, followed by a comprehensive feature screening for each index. We constructed fifteen distinct combinations of indices and selected four machine learning algorithms for model development. …”
Get full text

The systemic oxidative stress index predicts clinical outcomes of esophageal squamous cell carcinoma receiving neoadjuvant immunochemotherapy by Jifeng Feng, Jifeng Feng, Liang Wang, Xun Yang, Qixun Chen, Qixun Chen

“…Then, a new staging that included TNM and SOSI based on RPA algorithms was produced. In terms of prognostication, the RPA model performed significantly better than TNM classification.ConclusionSOSI is a simple and useful score based on available SOS-related indices. …”
Get full text

Machine learning identification of key genes in cardioembolic stroke and atherosclerosis: their association with pan-cancer and immune cells by Tianxiang Zhang, Chunhui Yuan, Mo Chen, Jinjiang Liu, Wei Shao, Ning Cheng

“…Two machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were used to screen for overlapping FRDEGs in CS and AS. …”
Get full text

Elucidating the dynamic tumor microenvironment through deep transcriptomic analysis and therapeutic implication of MRE11 expression patterns in hepatocellular carcinoma by Ruiqiu Chen, Chaohui Xiao, Zizheng Wang, Guineng Zeng, Shaoming Song, Gong Zhang, Lin Zhu, Penghui Yang, Rong Liu

“…Publicly available single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics were utilized to explore MRE11’s dynamic mechanisms in the tumor microenvironment (TME) of both primary and post-immunotherapy cases. We also screened for differentially expressed genes and constructed a robust HCC prognosis model using 101 machine-learning algorithms. …”
Get full text

Unraveling shared diagnostic genes and cellular microenvironmental changes in endometriosis and recurrent implantation failure through multi-omics analysis by Dongxu Qin, Yongquan Zheng, Libo Wang, Zhenyi Lin, Yao Yao, Weidong Fei, Caihong Zheng

“…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify key genes. Machine learning algorithms, including Random Forest (RF) and XGBoost, were utilized to screen for shared diagnostic genes, which were subsequently validated through receiver operating characteristic (ROC) analysis and clinical prediction models. …”
Get full text