Land Cover Transformations in Mining-Influenced Areas Using PlanetScope Imagery, Spectral Indices, and Machine Learning: A Case Study in the Hinterlands de Pernambuco, Brazil by Admilson da Penha Pacheco, João Alexandre Silva do Nascimento, Antonio Miguel Ruiz-Armenteros, Ubiratan Joaquim da Silva Junior, Juarez Antonio da Silva Junior, Leidjane Maria Maciel de Oliveira, Sylvana Melo dos Santos, Fernando Dacal Reis Filho, Carlos Alberto Pessoa Mello Galdino

“…The methodology consisted of monitoring and evaluating environmental impacts using the k-Nearest Neighbors (kNN) algorithm, spectral indices (Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and hydrological data, covering the period from 2018 to 2023. …”
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Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration by Maoqiang Lin, Maoqiang Lin, Shaolong Li, Yabin Wang, Yabin Wang, Guan Zheng, Guan Zheng, Fukang Hu, Fukang Hu, Qiang Zhang, Qiang Zhang, Pengjie Song, Haiyu Zhou, Haiyu Zhou

“…Subsequently, four machine learning algorithms (SVM-RFE, Random Forest, XGB, and GLM) were used to select the method to construct the diagnostic model. …”
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Enhancing ROP plus form diagnosis: An automatic blood vessel segmentation approach for newborn fundus images by José Almeida, Jan Kubicek, Marek Penhaker, Martin Cerny, Martin Augustynek, Alice Varysova, Avinash Bansal, Juraj Timkovic

“…The segmentation pipeline is combined with different pre-trained Convolution Neural Network architectures to evaluate its automatic classification capabilities. …”
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Artificial Intelligence–Enabled ECG Screening for LVSD in LBBB by Hak Seung Lee, MD, Sooyeon Lee, MD, Sora Kang, MS, Ga In Han, MS, Ah-Hyun Yoo, MS, Jong-Hwan Jang, PhD, Yong-Yeon Jo, PhD, Jeong Min Son, MD, Min Sung Lee, MD, MS, Joon-myoung Kwon, MD, MS, Kyung-Hee Kim, MD, PhD

“…Objectives: This study evaluates 4 AI-ECG models for detecting LVSD in LBBB patients and examines the impact of training cohort definitions. …”
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A fully automated, expert-perceptive image quality assessment system for whole-body [18F]FDG PET/CT by Cong Zhang, Xin Gao, Xuebin Zheng, Jun Xie, Gang Feng, Yunchao Bao, Pengchen Gu, Chuan He, Ruimin Wang, Jiahe Tian

“…Automated identification and localization algorithms were applied to select predefined pairs of PET and CT slices from whole-body images. …”
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The Persistent Threat of Chronic Inflammation on the Mortality Among Cervical Cancer Survivors: A Mendelian Randomization and Machine Learning Analysis Using UK Biobank and Chinese... by Wang J, Chen Z, Guan M, Ma Z, Peng L, Chen J, Fiori PL, Carru C, Capobianco G, Coradduzza D, Zhou L

“…We aimed to comprehensively evaluate the genetic association between inflammation and cervical cancer, and construct an accurate prognosis model based on circulating inflammatory parameters and indexes with machine learning (ML) algorithms.Patients and Methods: We tested the genome-wide association of circulating inflammatory molecules (CIMs) (91 circulating inflammatory cytokines and 10 inflammatory cells) and summary data retrieved from the UK biobank (cases = 1659 and controls =381,902) with two-sample Mendelian randomization (MR) and colocalization analyses. …”
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Efficacy and predictive biomarkers of immunotherapy in Epstein-Barr virus-associated gastric cancer by Lin Shen, Xiaochen Zhao, Zhenghang Wang, Yuezong Bai, Feilong Zhao, Zhi Peng, Jinping Cai, Tong Xie, Shuang Tong, Xiaofan Wei

“…Herein, we sought to investigate the efficacy and potential biomarkers of ICB in EBVaGC identified by next-generation sequencing (NGS).Design An NGS-based algorithm for detecting EBV was established and validated using two independent GC cohorts (124 in the training cohort and 76 in the validation cohort). …”
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Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer by Pu Zhou, Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He, Ying He

“…The clinical feature prediction model using the GBM algorithm had an AUC of 0.819 and an accuracy of 0.739. …”
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Automated sample annotation for diabetes mellitus in healthcare integrated biobanking by Johannes Stolp, Christoph Weber, Danny Ammon, André Scherag, Claudia Fischer, Christof Kloos, Gunter Wolf, P. Christian Schulze, Utz Settmacher, Michael Bauer, Andreas Stallmach, Michael Kiehntopf, Boris Betz

“…Performance was compared with a simple laboratory cut-off classifier (LCC) and a logistic regression (LR) model. Algorithms based on laboratory values, ICD-10 codes or information from discharge summaries extracted by a natural language processing software (NLP-DS) were evaluated as a second (review) step designed to increase the precision of annotations. …”
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Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP by Bingkui Ren, Yuping Zhang, Siying Chen, Jinglong Dai, Junci Chong, Yifei Zhong, Mengkai Deng, Shaobo Jiang, Zhigang Chang

“…Methods In this retrospective cohort study, we derived data from the eICU Collaborative Research Database (eICU-CRD) to develop and evaluate a predictive model. ​Clinical data from the first 24 h of ICU admission were extracted, and the dataset was randomly split into training (80%) and validation (20%) sets. …”
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ImmuProgML: machine learning-based dissection of cancer-immune dynamics during tumor progression to improve immunotherapy by Hanxiao Zhou, Lan Mei, Qianyi Lu, Yakun Zhang, Yue Sun, Caiyu Zhang, Han Jiang, Jiajun Zhou, Xia Li, Yunpeng Zhang, Shangwei Ning

“…We introduced the DNEX score, which combines expression changes with immunotherapy-driven network topologies, and employed machine learning algorithms for prognostic and immunotherapy response predictions. …”
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Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment by Du S, Wu Y, Tao J, Shu L, Yan T, Xiao B, Lv S, Ye M, Gong Y, Zhu X, Hu P, Wu M

“…We randomly assigned these patients to training and validation cohorts with a ratio of 7:3. …”
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Application of Intravoxel Incoherent Motion in the Prediction of Intra-Tumoral Tertiary Lymphoid Structures in Hepatocellular Carcinoma by Ma L, Liao S, Zhang X, Zhou F, Geng Z, Hu J, Zhang Y, Zhang C, Meng T, Wang S, Xie C

“…The recurrence-free survival (RFS) was evaluated with Kaplan–Meier curves.Results: A total of 168 patients were divided into training (n=128) and testing (n=40) cohorts (mean age: 56.83± 14.43 years; 149 [88.69%] males; 130 TLSs+). …”
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‘Machine Learning’ multiclassification for stage diagnosis of Alzheimer’s disease utilizing augmented blood gene expression and feature fusion by Manash Sarma, Subarna Chatterjee

“…Because of high data imbalance in genomic data, border line oversampling is explored for model training and original data for validation. We have conducted a multimodal analysis and stage classification by integrating the ADNI gene expression and clinical datasets using ‘Feature-Level Fusion’. …”
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Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study by Juan Chen, Xiong Chen, Kai Fu, Lan Zhou, Shichao Long, Mengsi Li, Linhui Zhong, Aerzuguli Abudulimu, Wenguang Liu, Deng Pan, Ganmian Dai, Yigang Pei, Wenzheng Li

“…Radiomic features were extracted from intratumoral and peritumoral regions of interest and analyzed using machine learning algorithms to develop a predictive classifier. The classifier’s performance was evaluated using the area under the curve (AUC), with prognostic and predictive value assessed across four independent cohorts and in a dual-center outcome cohort of 41 patients who received immunotherapy.Results Patients with HCC and a high pTLS density experienced prolonged median overall survival (p<0.05) and favorable immunotherapy response (p=0.03). …”
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Assessment of prostate cancer aggressiveness through the combined analysis of prostate MRI and 2.5D deep learning models by Yalei Wang, Yuqing Xin, Baoqi Zhang, Fuqiang Pan, Xu Li, Manman Zhang, Yushan Yuan, Lei Zhang, Peiqi Ma, Bo Guan, Yang Zhang

“…Models were constructed using the LightGBM algorithm: a radiomic feature model, a deep learning feature model, and a combined model integrating radiomic and deep learning features. …”
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A study on early diagnosis for fracture non-union prediction using deep learning and bone morphometric parameters by Hui Yu, Qiyue Mu, Zhi Wang, Yu Guo, Jing Zhao, Guangpu Wang, Qingsong Wang, Xianghong Meng, Xiaoman Dong, Shuo Wang, Jinglai Sun

“…And we extracted bone morphometric parameters to establish an early diagnostic evaluation system for the non-union of fractures.ResultsA dataset comprising 2,448 micro-CT images of the rat fracture lesions with fracture Region of Interest (ROI), bone callus and healing characteristics was established and used to train and test the proposed VM-TE-UNet which achieved a Dice Similarity Coefficient of 0.809, an improvement over the baseline's 0.765, and reduced the 95th Hausdorff Distance to 13.1. …”
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Hierarchical diagnosis of breast phyllodes tumors enabled by deep learning of ultrasound images: a retrospective multi-center study by Yuqi Yan, Yuanzhen Liu, Yao Wang, Tian Jiang, Jiayu Xie, Yahan Zhou, Xin Liu, Meiying Yan, Qiuqing Zheng, Haifei Xu, Jinxiao Chen, Lin Sui, Chen Chen, RongRong Ru, Kai Wang, Anli Zhao, Shiyan Li, Ying Zhu, Yang Zhang, Vicky Yang Wang, Dong Xu

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Advanced quantification pipeline reveals new spatial and temporal tumor characteristics in preclinical multiple myeloma by Zhixin Sun, Jacqueline M. Godbe, Alexander Zheleznyak, Brad Manion, Junhao Hu, Julie L. Prior, Kathleen Duncan, Ulugbek S. Kamilov, Monica Shokeen

“…An Attention U-Net was trained to segment the thoracolumbar spine, pelvis and pelvic joints, sacrum, and femurs from 2D CT slices. …”
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MRI-based intra-tumoral ecological diversity features and temporal characteristics for predicting microvascular invasion in hepatocellular carcinoma by Yuli Zeng, Huiqin Wu, Yanqiu Zhu, Chao Li, Dongyang Du, Yang Song, Sulian Su, Jie Qin, Guihua Jiang, Guihua Jiang, Guihua Jiang

“…A clinical-radiological model (CR model) was constructed, and two fusion models were generated by combining the radiomics or/and CR models using a stacking algorithm (fusion_R and fusion_CR). Model performance was evaluated using AUC, accuracy, sensitivity, and specificity.ResultsThe MDelta model demonstrated higher sensitivity compared to the MCVT-AP and MCVT-PVP models. …”
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