Domain adaptation of deep neural networks for tree part segmentation using synthetic forest trees by Mitch Bryson, Ahalya Ravendran, Celine Mercier, Tancred Frickey, Sadeepa Jayathunga, Grant Pearse, Robin J.L. Hartley

“…We develop a new pipeline for generating high-fidelity simulated LiDAR scans of synthetic forest trees and combine this with an unsupervised domain adaptation strategy to adapt models trained on synthetic data to LiDAR data captured in real forest environments.Models were trained for semantic segmentation of tree parts using a PointNet++ architecture and evaluated across a range of medium to high-resolution laser scanning datasets collected across both ground-based and aerial platforms in multiple forest environments. …”
Get full text

Multimodal ultrasound radiomics containing microflow images for the prediction of central lymph node metastasis in papillary thyroid carcinoma by Jiangyuan Ben, Jiangyuan Ben, Qiying Yv, Pengfei Zhu, Junhao Ren, Pu Zhou, Guifang Chen, Ying He, Ying He

“…Among the prediction models, the fusion model constructed by Multilayer Perceptron (MLP) algorithm showed the best diagnostic performance, outperforming the other models in both the training cohort (AUC = 0.886) and the testing cohort (AUC = 0.873).ConclusionsThe fusion model based on clinical data and multimodal ultrasound radiomics has better predictive ability and net clinical benefit for CLNM in patients with PTC, confirms the diagnostic value of microflow images for CLNM, and can help to evaluate patients’ preoperative lymph node status and make the correct decision on the surgical procedure.…”
Get full text

Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn’s disease by Zhuoyan Chen, Weimin Cai, Yuanhang He, Tianhao Mei, Yuxuan Zhang, Shiyu Li, Yiwen Hong, Yuhao Chen, Huiya Ying, Yuan Zeng, Fujun Yu

“…Z score standardization and independent sample t test were applied to identify optimal predictive features, which were then utilized in seven ML algorithms for training and validation. Model performance was assessed through receiver-operating characteristic curves, precision–recall curves, and calibration curve analyses, evaluating accuracy and clinical applicability. …”
Get full text

Machine Learning Techniques to Model and Predict Airflow Requirements in Underground Mining by Maria Karagianni, Andreas Benardos

“…With this twin model, several scenarios are developed and evaluated and more importantly data are gathered, allowing for the training of the ML algorithms used to assess and predict the required ventilation airflow, taking into account air quality data, the number of workers, and machine fleet.…”
Get full text

Predicting postoperative malnutrition in patients with oral cancer: development of an XGBoost model with SHAP analysis and web-based application by Lixia Kuang, Lixia Kuang, Jingya Yu, Yunyu Zhou, Yu Zhang, Yu Zhang, Guangman Wang, Guangman Wang, Fangmin Zhang, Grace Paka Lubamba, Grace Paka Lubamba, Xiaoqin Bi, Xiaoqin Bi

“…The dataset was divided into a training set (70%) and a validation set (30%). Predictive models were developed via four supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost). …”
Get full text

Major Adverse Kidney Events in Hospitalized Older Patients With Acute Kidney Injury: Machine Learning–Based Model Development and Validation Study by Xiao-Qin Luo, Ning-Ya Zhang, Ying-Hao Deng, Hong-Shen Wang, Yi-Xin Kang, Shao-Bin Duan

“…The eXtreme Gradient Boosting algorithm was applied to establish a prediction model for MAKE30. …”
Get full text

Data-Driven Optimised XGBoost for Predicting the Performance of Axial Load Bearing Capacity of Fully Cementitious Grouted Rock Bolting Systems by Behshad Jodeiri Shokri, Ali Mirzaghorbanali, Kevin McDougall, Warna Karunasena, Hadi Nourizadeh, Shima Entezam, Shahab Hosseini, Naj Aziz

“…For this purpose, after building the dataset and dividing it randomly into two training and testing datasets, five different XGBoost models were developed: a standalone XGBoost model and four hybrid models incorporating Harris hawk optimisation (HHO), the jellyfish search optimiser (JSO), the dragonfly algorithm (DA), and the firefly algorithm (FA). …”
Get full text

Characterizing low femoral neck BMD in Qatar Biobank participants using machine learning models by Nedhal Al-Husaini, Rozaimi Razali, Amal Al-Haidose, Mohammed Al-Hamdani, Atiyeh M. Abdallah

“…The cohort was split 60% and 40% for training and validation, respectively. Logistic regression algorithms were implemented to predict femoral neck BMD, and the area under the curve (AUC) was used to evaluate model performance. …”
Get full text

Intrusion detection system based on machine learning using least square support vector machine by Pratik Waghmode, Manideep Kanumuri, Hosam El-Ocla, Tanner Boyle

“…In this paper, the exhaustive feature selection algorithm is employed to assess every possible combination of features in a dataset to evaluate its performance. …”
Get full text

Preoperative prediction of recurrence risk factors in operable cervical cancer based on clinical-radiomics features by Xue Du, Xue Du, Chunbao Chen, Lu Yang, Yu Cui, Min Li

“…Receiver operating characteristic (ROC), DeLong test, calibration curve (CC), and decision curve (DC) were used to evaluate the predictive performance and clinical benefit of the model.ResultA total of 99 patients with cervical cancer were included in this study, with 79 and 20 cases in the training and test groups, respectively. …”
Get full text

Magnetic Resonance Imaging Texture Analysis Based on Intraosseous and Extraosseous Lesions to Predict Prognosis in Patients with Osteosarcoma by Yu Mori, Hainan Ren, Naoko Mori, Munenori Watanuki, Shin Hitachi, Mika Watanabe, Shunji Mugikura, Kei Takase

“…The area under the receiver operating characteristic curve (AUC) was calculated to evaluate diagnostic performance in evaluating histological patterns and 3-year survival. …”
Get full text

Preoperative Prediction of Macrotrabecular-Massive Hepatocellular Carcinoma Using Machine Learning-Based Ultrasomics by Li Y, Duan S, Ren S, Li D, Ma Y, Bu D, Liu Y, Li X, Cai X, Zhang L

“…Ultrasomics models were constructed based on the ultrasound image features of the training set using five different ML algorithms, including random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), decision tree (DT), and logistic regression (LR). …”
Get full text

Role of Artificial Intelligence in Minimizing Missed and Undiagnosed Fractures Among Trainee Residents by Sadat-Ali M, Al Omar HK, Alneghaimshi MM, AlHossan AM, Baragabh AM

“…Mir Sadat-Ali,1 Hussain Khalil Al Omar,2 Muath M Alneghaimshi,2 Abdallah M AlHossan,3 Abdullah M Baragabh2 1Department of Orthopaedic Surgery, Haifa Medical Complex, Alkhobar, Saudi Arabia; 2King Fahad Military Medical Complex, Ministry of Defense Health Services, Dhahran, Saudi Arabia; 3King Fahad Military Medical Complex, Ministry of Defense Health Services, Dhahran and Alfaisal University, Riyadh, Saudi ArabiaCorrespondence: Mir Sadat-Ali, Haifa Medical Complex, 7200 King Khalid Road, AlKhozama, Alklhobar, 32424, Saudi Arabia, Tel +966505848281, Email drsadat@hotmail.comBackground and Objectives: Traumatic Fractures and dislocations are missed up to 10% at the first line of defense in the emergency room and by the junior orthopedic residents in training. This review was done to evaluate the accuracy of AI-assisted fracture detection and to compare with the residents in training.Methods: We searched all related electronic databases for English language literature between January 2015 and July 2023, Pub Med, Scopus, Web of Science, Cochrane Central Ovid Medline, Ovid Embase, EBSCO Cumulative Index to Allied Health Literature, with keywords of Artificial Intelligence, fractures, dislocations, X-rays, radiographs and missed diagnosis. …”
Get full text

IVIM-DWI-based radiomic model for preoperative prediction of hepatocellular carcinoma differentiation by ZHUANG Yuxiang, LI Xiaofeng, ZHOU Daiquan

“…In the comparison between the radiomic model constructed by SVM algorithm and the radiomics-clinical combined model, the AUC value was 0.954 (0.908~1.000) for the former model, and was 0.943 (0.905~0.982) for the latter model in the training set, and there was no significant difference between them. …”
Get full text

Predicting antiretroviral therapy adherence status of adult HIV-positive patients using machine-learning Northwest, Ethiopia, 2025 by Kelemua Aschale Yeneakal, Gizaw Hailiye Teferi, Temesgen T. Mihret, Abraham Keffale Mengistu, Sefefe Birhanu Tizie, Maru Meseret Tadele

“…Seven machine learning algorithms: support vector machine, random forest, decision tree, logistic regression, gradient boosting, K-nearest neighbors, and artificial neural network were trained. …”
Get full text

Continuous Real-Time Acoustic Monitoring of endangered bird species in Hawai‘i by Melissa Weidlich-Rau, Amanda K. Navine, Patrick T. Chaopricha, Felix Günther, Stefan Kahl, Thomas Wilhelm-Stein, Raymond C. Mack, Hendrik Reers, Aaron N. Rice, Maximilian Eibl, Patrick J. Hart, Patrick Wolff, Holger Klinck, Lena D. Schnell, Rogelio Doratt, Michael Loquet, Tiana Lackey

“…The system is based on the BirdNET algorithm and was evaluated with over 16,000 soundscape recordings from Hawai‘i. …”
Get full text

Assessing the predictive value of time-in-range level for the risk of postoperative infection in patients with type 2 diabetes: a cohort study by Ying Wu, Rui Xv, Qinyun Chen, Ranran Zhang, Min Li, Chen Shao, Guoxi Jin, Guoxi Jin, Xiaolei Hu, Xiaolei Hu

“…LASSO regression and the Boruta algorithm were used to screen out the predictive factors related to postoperative infection in T2DM patients in the training set. …”
Get full text

Identification of serum tRNA-derived small RNAs biosignature for diagnosis of tuberculosis by Zikun Huang, Qing Luo, Cuifen Xiong, Haiyan Zhu, Chao Yu, Jianqing Xu, Yiping Peng, Junming Li, Aiping Le

“…By utilizing cross-validation with a random forest algorithm approach, the training cohort achieved a sensitivity of 100% and specificity of 100%. …”
Get full text

Combined impact of semantic segmentation and quantitative structure modelling of Southern pine trees using terrestrial laser scanning by Jinyi Xia, Timothy A. Martin, Gary F. Peter, Kody M. Brock, Jeff W. Atkins, Matthew A. Gitzendanner, Inacio Bueno, Kim Calders, Ana Paula Dalla Corte, Andrew T. Hudak, Monique Bohora Schlickmann, Michael G. Andreu, Caio Hamamura, Carine Klauberg, Carlos A. Silva

“…Addressing this gap, our study evaluates the performance of multiple segmentation algorithms on TLS data from southern pines, providing valuable insights into improving structural analysis and supporting more precise and efficient forest research and monitoring methodologies. …”
Get full text

Machine learning based screening of biomarkers associated with cell death and immunosuppression of multiple life stages sepsis populations by Jie Yang, Fanyan Ou, Binbin Li, Lixiong Zeng, Qiuli Chen, Houyu Gan, Jianing Yu, Qian Guo, Jihua Feng, Jianfeng Zhang

“…Nine machine learning algorithms (Logistic Regression LR, Decision Tree DT, Gradient Boosting Machine GBM, K-Nearest Neighbors KNN, LASSO, Principal Component Analysis PCA, Random Forest RF, Support Vector Machine SVM, and XGBoost) were applied to training and testing datasets with 10-fold cross-validation to select three optimized algorithm models. …”
Get full text