Inference of three-dimensional hot-spot and shell morphology in inertial confinement fusion experiments using a convolutional neural network by K. M. Woo, J. Buck, K. Churnetski, R. Betti, C. A. Thomas, C. Stoeckl, P. V. Heuer, C. Kanan, P. B. Radha, J. Carroll-Nellenback, K. S. Anderson, T. J. B. Collins, L. Ceurvorst, V. Gopalaswamy, S. T. Ivancic

“…The shell configuration is inferred indirectly through machine learning using a convolutional neural network extensively trained on a dec3d simulation database. This simulation-dependent approach yields consistent agreement between reconstructed 3D shell densities and machine-learning optimized dec3d simulation results. …”
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Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network by Xingyu Duan, Xiaojun Ma, Mengqi Zhu, Linan Wang, Dingqi You, Lili Deng, Ningkui Niu

“…The training set was used to train the Dual AttentionUNet model, and the validation set was used to fine-tune hyperparameters and prevent overfitting during training. …”
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LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns by Mehran Motamedi, Reza Shidpour, Mehdi Ezoji

“…Abstract In this paper, we present a machine learning-based approach that leverages Long Short-Term Memory (LSTM) networks combined with a sliding window technique for feature extraction, aimed at accurately predicting point defect percentages in semiconductor materials based on simulated X-ray Diffraction (XRD) data. The model was initially trained on silicon-simulated XRD data with defect percentages ranging from 1 to 5%, enabling it to predict defect percentages from 0 to 10% in silicon and other semiconductor materials, including AlAs, CdS, GaAs, Ge, and ZnS. …”
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Lung Ultrasound in Neonatal Respiratory Distress Syndrome: A Narrative Review of the Last 10 Years by Federico Costa, Annachiara Titolo, Mandy Ferrocino, Eleonora Biagi, Valentina Dell’Orto, Serafina Perrone, Susanna Esposito

“…Future research should prioritize standardizing training and scoring protocols to facilitate wider implementation and optimize neonatal respiratory care outcomes.…”
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Multiple Free Flaps and Second Toe Transfer to Salvage Grasp Function in Bilateral Complete Degloved Hands by R. Srikanth, D. Mukunda Reddy, K. Muralimohana Reddy, N. Rambabu, V. Ganga Kishore, Sandeep Naidu

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Exploring ChatGPT’s Efficacy in Orthopaedic Arthroplasty Questions Compared to Adult Reconstruction Surgeons by Benjamin Nieves-Lopez, BS, Clayton Wing, MD, Bryan D. Springer, MD, Keith T. Aziz, MD

“…Background: Chat Generative Pre-trained Transformer (ChatGPT) is a language model designed to conduct conversations utilizing extensive data from the internet. …”
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Improving resectable gastric cancer prognosis prediction: A machine learning analysis combining clinical features and body composition radiomics by Gianni S.S. Liveraro, Maria E.S. Takahashi, Fabiana Lascala, Luiz R. Lopes, Nelson A. Andreollo, Maria C.S. Mendes, Jun Takahashi, José B.C. Carvalheira

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Breast Tumor-Like-Masses Segmentation From Scattering Images Obtained With an Ultrahigh-Sensitivity Talbot-Lau Interferometer Using Convolutional Neural Networks by Ionut-Cristian Ciobanu, Nicoleta Safca, Elena Anghel, Dan Popescu

“…Five CNN architectures - U-Net, ResNet50, DeepLabV3, PSPNet, and SegNet -were trained and tested on an augmented dataset of 320 images. …”
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Attention-enhanced hybrid CNN–LSTM network with self-adaptive CBAM for COVID-19 diagnosis by Fatin Nabilah Shaari, Aimi Salihah Abdul Nasir, Wan Azani Mustafa, Wan Aireene Wan Ahmed, Abdul Syafiq Abdull Sukor

“…Accurate identification of COVID-19 still presents difficulties due to the limitations of RT-PCR testing, such as reduced sensitivity and restricted availability. Chest X-Ray (CXR) imaging modalities, combined with deep learning models, offer a non-invasive solution. …”
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Machine Learning-Based Interpretable Screening for Osteoporosis in Tuberculosis Spondylitis Patients Using Blood Test Data: Development and External Validation of a Novel Web-Based... by Yasin P, Ding L, Mamat M, Guo W, Song X

“…Multiple machine learning (ML) algorithms, including logistic regression, random forest, and XGBoost, were trained and optimized using nested cross-validation and hyperparameter tuning. …”
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ChatGPT for the Standardized Operative Notes in Plastic Surgery by Fizzah Arif

“… • All necessary investigations, including complete blood count, coagulation profile, and chest x-ray were done and found to be within normal limits…”
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Machine learning-based prediction of physical parameters in heterogeneous carbonate reservoirs using well log data by Fuyong Wang, Xianmu Hou

“…The methodology involves classifying pore-permeability types based on the flow index, leveraging logging curves and geological data. Models are trained using seven logging parameters—spectral gamma rays (SGR), uranium-free gamma rays (CGR), photoelectric absorption cross-section index (PE), lithologic density (RHOB), acoustic transit time (DT), neutron porosity (NPHI), and formation true resistivity (RT)—along with corresponding physical property labels. …”
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Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings by Rahul Haripriya, Nilay Khare, Manish Pandey

“…This study investigates the integration of transfer learning and federated learning for privacy-preserving medical image classification using GoogLeNet and VGG16 as baseline models to evaluate the generalizability of the proposed framework. Pre-trained on ImageNet and fine-tuned on three specialized medical datasets for TB chest X-rays, brain tumor MRI scans, and diabetic retinopathy images, these models achieved high classification accuracy across various aggregation methods. …”
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Impact of the COVID-19 Pandemic on Buckle Fracture Treatment by Mehmet E. Kilinc, BS, Evan P. Sandefur, BS, Mosufa Zainab, BS, Nicholas J. Peterman, MD, Andrea A. Yu-Shan, MSHS, Peter J. Apel, MD, PhD

“…The provider type was documented and subclassified into fellowship-trained pediatric orthopaedic surgeons, non-pediatric fellowship-trained orthopaedic surgeons, and non-physicians. …”
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Artificial intelligence based surgical support for experimental laparoscopic Nissen fundoplication by Holger Till, Ciro Esposito, Chung Kwong Yeung, Dariusz Patkowski, Sameh Shehata, Steve Rothenberg, Georg Singer, Tristan Till

“…Especially in medical imaging, AI/CV holds significant promise analyzing data from x-rays, CT scans, and MRIs. However, the application of AI/CV to support surgery has progressed more slowly. …”
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MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification by Md. Eshmam Rayed, Jamin Rahman Jim, Md Juniadul Islam, M. F. Mridha, Md Mohsin Kabir, Md. Jakir Hossen

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A privacy-enhanced framework for collaborative Big Data analysis in healthcare using adaptive federated learning aggregation by Rahul Haripriya, Nilay Khare, Manish Pandey, Sreemoyee Biswas

“…This study explores a federated learning approach combined with transfer learning to enhance privacy in medical image classification using ResNet and VGG16 architectures. Pre-trained on ImageNet and fine tuned on three specialized medical datasets TB chest X-rays, brain tumor MRI scans, and diabetic retinopathy images these models were deployed in a simulated multi-center healthcare environment. …”
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