Assessing effectiveness of ABCDE Framework for teaching condylar fracture reduction in dental education: a mixed methods study by Jun Pang, Zhigan Lv, Haifeng Zhang, Siyao Yang, Yuanyuan Wang, Yang Wu, Xiaoxing Hao, Lin Cheng, Pengfei Xin

“…Methods The ABCDE framework includes Assessment, Briefing, Collaborative learning, Demonstration, and Evaluation phases. …”
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Masked pre-training of transformers for histology image analysis by Shuai Jiang, Liesbeth Hondelink, Arief A. Suriawinata, Saeed Hassanpour

“…The pre-trained MaskHIT surpasses various multiple instance learning approaches by 3% and 2% on survival prediction and cancer subtype classification tasks, and also outperforms recent state-of-the-art transformer-based methods. …”
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Integration of an Audiovisual Learning Resource in a Podiatric Medical Infectious Disease Course: Multiple Cohort Pilot Study by Garrik Hoyt, Chandra Shekhar Bakshi, Paramita Basu

“…These results contribute to the discourse on innovative educational methods and highlight the potential of multimedia-based learning resources to enrich medical curricula. …”
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Building virtual communities of practice in post-editing training: A mixed-method quasi-experimental study by Lyu Wang, Xiangling Wang

“…Students' perceptions of the VCoP method were generally positive, showing its usability.…”
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Indoor Multidimensional Reconstruction Based on Maximal Cliques by Yongtong Zhu, Lei Li, Na Liu, Qingdu Li, Ye Yuan

“…Through extensive experimentation, our method demonstrates a significant reduction in processing time, taking approximately one-tenth of the time required by the original method without a decline in accuracy. …”
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Implementation of a tele-paediatric network in hospitals in a rural region: A mixed methods implementation study by Nils Pfeuffer, Angelika Beyer, Luisa Tischler, Sarah Heimbuch, Heiko Krause, Markus Krohn, Steffen Fleßa, Thomas Ruppel, Wolfgang Hoffmann, Neeltje van den Berg

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Using Artificial Intelligence for Detecting Diabetic Foot Osteomyelitis: Validation of Deep Learning Model for Plain Radiograph Interpretation by Francisco Javier Álvaro-Afonso, Aroa Tardáguila-García, Mateo López-Moral, Irene Sanz-Corbalán, Esther García-Morales, José Luis Lázaro-Martínez

“…Objective: To develop and validate a ResNet-50-based deep learning model for automatic detection of osteomyelitis (DFO) in plain radiographs of patients with diabetic foot ulcers (DFUs). …”
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Diagnosis of clear cell renal cell carcinoma via a deep learning model with whole-slide images by Weixing Jiang, Siyu Qi, Cancan Chen, Wenying Wang, Xi Chen

“…Conclusion: The use of a deep learning method for the diagnosis of ccRCC is feasible. …”
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A Guided-Ensembling Approach for Cell Counting in Fluorescence Microscopy Images by C. Emre Dedeagac, Can F. Koyuncu, Michelle M. Adams, Cagatay Edemen, Berk C. Ugurdag, N. Ilgim Ardic-Avci, H. Fatih Ugurdag

“…Although deep learning and computer vision based approaches have demonstrated success in the field of cell counting and detection in microscopic images, they continue to have certain limitations. …”
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Interpretation and understanding of asphalt crack detection deep learning models using integrated gradient (I.G.) maps by Gihan P. Ruwanpathirana, Sadeep Thilakarathna, Shanaka Kristombu Baduge

“…In this study, we employed Integrated Gradient (I.G.) maps to elucidate the workings of these models and interpret CNN-based crack image voxels that contributed to the positive (cracked) output of CNN. …”
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Gincana para el estudio de la cardiología: un viaje lúdico hacia el aprendizaje significativo en el grado en Medicina by Sonia Velasco del Castillo, Rodrigo Damián García, Laura Pacios Arcay, Enara Azpitarte Cortés, Susana Romero Yesa, Alberto Ullate de la Torre, Idoia Bravo Martinez, Unai Ortega Mera

“…Within Miller's pyramid, this practice focused on the “how to do” level. Materials and methods: The method used was based on the University of Deusto Learning Model. …”
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A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI by M Nisha, T Kannan, K Sivasankari

“…To assess its effectiveness, the proposed segmentation technique was evaluated on a large dataset of 18,900 images from Kaggle, and the results were compared with those of existing methods. Based on the analysis of results reported in the experimental section, the proposed scheme in the Semi-Supervised Deep Neuro-Fuzzy Iterative Learning System (SS-DNFIL) achieved a 0.97 Dice coefficient, a 0.93 Jaccard coefficient, a 0.95 sensitivity (true positive rate), a 0.97 specificity (true negative rate), a false positive value of 0.09 and a 0.08 false negative value when compared to existing approaches. …”
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Predictive performance and uncertainty analysis of ensemble models in gully erosion susceptibility assessment by Congtan Liu, Haoming Fan, Yixuan Wang

“…This study aims to identify the optimal feature datasets and to quantify the uncertainty associated with gully erosion prediction models by developing a novel methodological framework based on ensembles of the three machine learning models: Random Forest (RF), Convolutional Neural Network (CNN), and Transformer models. …”
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Unveiling spatiotemporal evolution and driving factors of ecosystem service value: interpretable HGB-SHAP machine learning model by Xiangming Xu, Xinyi Zhang, Linghua Qin, Rui Li

“…Furthermore, the driving factors of ESV were explored using the explainable machine learning method.ResultsThe findings are as follows: (1) The net ESV of the Gangjiang Upstream Basin (GUB) has undergone a decline from 1990 to 2000, with climate regulation and hydrological regulation collectively accounting for approximately 50% of all functions. (2) A mere 0.69% of the areas exhibited an increase in the level of ESV, while 11.19% demonstrated a decline by 2020, based on the grid scale. …”
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Leveraging machine learning models for anemia severity detection among pregnant women following ANC: Ethiopian context by Bekan Kitaw, Chera Asefa, Firew Legese

“…Feature selection employed filter methods based on mutual information, and F-score was used to assess anemia severity prediction across four classes. …”
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A multilayer deep autoencoder approach for cross layer IoT attack detection using deep learning algorithms by K. Saranya, A. Valarmathi

“…To improve detection and adapt to emerging attack methods, the M-LDAE system employs deep learning algorithms such as RNNs, GNNs, and TCNs. …”
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Predictive study of machine learning combined with serum Neuregulin 4 levels for hyperthyroidism in type II diabetes mellitus by Huilan Gu, Ye Lu

“…Machine learning techniques have garnered widespread attention due to their advantages in modeling high-dimensional, heterogeneous data.ObjectiveThis study was to evaluate the predictive capability of a support vector machine (SVM) model based on serum NRG4 combined with a convolutional neural network (CNN) and long short-term memory network (LSTM)-based ultrasound feature classification (SVM-CNN+LSTM) model for predicting the occurrence of FT in patients with T2DM.MethodsStudied 500 T2DM patients (60 with FT, 440 without), and 200 healthy controls. …”
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Improving interprofessional collaboration in pain clinics through simulation: a longitudinal Readiness for Interprofessional Learning Scale assessment by John Mekail, Ysaac Zegeye, Quinn Lanners, Muhammad Farooq Anwar, Peter K Yi

“…Despite its importance, IPC within outpatient pain medicine remains understudied, and the Readiness for Interprofessional Learning Scale (RIPLS) has not been used longitudinally in outpatient pain medicine.Objectives The primary objective of this quality improvement (QI) project was to evaluate and enhance readiness for interprofessional learning among clinical staff in an outpatient pain clinic, measured over 6 months in an outpatient pain clinic.Methods This initiative took place from October 2021 to April 2022 in an academic institution’s hospital-based outpatient pain clinic. …”
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CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li, Xinyu Zhao

“…To address these issues, we introduce a graph diffusion model into the field of CD and propose a novel network architecture called CGD-CD Net, which is driven by a structure-sensitive SSL strategy based on contrastive learning. Specifically, a superpixel segmentation algorithm is applied to bi-temporal images to construct graph nodes, while the k-nearest neighbors algorithm is used to define edge connections. …”
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Learning hand hygiene from the champions: Investigating key compliance facilitators among healthcare workers through interviews. by Charis von Auer, Magdalena Probst, Wulf Schneider-Bachart, Susanne Gaube

“…<h4>Methods</h4>In this qualitative study, we conducted problem-oriented semi-structured interviews with questions based on the 14 domains of the revised TDF. …”
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