Development and Validation of an Algorithm for Segmentation of the Prostate and its Zones from Three-dimensional Transrectal Multiparametric Ultrasound Images by Daniel L. van den Kroonenberg, Florian T. Delberghe, Auke Jager, Arnoud W. Postema, Harrie P. Beerlage, Wim Zwart, Massimo Mischi, Jorg R. Oddens

“…Automated prostate segmentation facilitates workflows, and zonal segmentation can aid in PC diagnosis, accounting for differences in imaging characteristics and tumor incidence. …”
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A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management by Muhammad Salman Haleem, Daphne Katsarou, Eleni I. Georga, George E. Dafoulas, Alexandra Bargiota, Laura Lopez-Perez, Miguel Rujas, Giuseppe Fico, Leandro Pecchia, Dimitrios Fotiadis, Gatekeeper Consortium

“…The CGM time series were processed using a stacked Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BiLSTM) network followed by an attention mechanism. …”
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Deep learning-based automated measurement of hip key angles and auxiliary diagnosis of developmental dysplasia of the hip by Ruixin Li, Xiao Wang, Tianran Li, Beibei Zhang, Xiaoming Liu, Wenhua Li, Qirui Sui

“…Results The results obtained from both manual measurements and the artificial intelligence model demonstrated no significant differences in the Sharp, Tönnis, and Center edge angles (all p > 0.05). …”
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Comment on S Memon, et al. (J Pak Med Assoc. 74: 1163-1166, June 2024) Osmolar gap in hyponatraemia: An exploratory study by Muhammad Ramish Irfan

“… Madam, Your paper about the osmolar gap in hyponatraemiawas much appreciated as it remains a subject shrouded inmisunderstanding.The observations reported in this paper are certainly thoughtprovoking, therefore I would extend a few conceptualclarifications that I believe your readership would benefit fromin gaining deeper insight about the findings reported in thisstudy.The difference between tonicity, osmolarity and osmolality isoften disregarded and appears convoluted however, it is crucialto delineate between these terms nevertheless. …”
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Mean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning model by Li-Hsin Cheng, Xiaowu Sun, Charlie Elliot, Robin Condliffe, David G. Kiely, Samer Alabed, Andrew J. Swift, Rob J. van der Geest, David G Kiely, Lisa Watson, Iain Armstrong, Catherine Billings, Athanasios Charalampopoulos, Robin Condliffe, Charlie Elliot, Abdul Hameed, Neil Hamilton, Judith Hurdman, Allan Lawrie, Robert A Lewis, Smitha Rajaram, Alex Rothman, Andy J. Swift, Steven Wood, AA Roger Thompson, Jim Wild

“…Methods: We constructed an explainable convolutional neural network (CNN) taking cardiac MR cine series from four different views as input to predict mean pulmonary artery pressure (mPAP). …”
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“Locality – Adaptation” Research of Hydropower Resettlement Communities in the Jinsha River Basin: A Case Study of Ludila Hydropower Station by Fang WANG, Zhuoqi LI, Haoyi XU, Jiaqi YAN

“…At the settlement scale, the Mask Region-based Convolutional Neural Network (Mask R-CNN) deep learning model is utilized to identify architectural spatial features, categorizing three typical building types: traditional pitched-roof buildings, uniformly planned flat-roof buildings, and color steel plate-modified structures. …”
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A Low Complexity Algorithm for 3D-HEVC Depth Map Intra Coding Based on MAD and ResNet by Erlin Tian, Jiabao Zhang, Qiuwen Zhang

“…In contrast, for complex CUs, we propose a lightweight ResNet (Residual Neural Network) model that substitutes standard convolutions with depthwise separable convolutions (DSC) in order to decrease the number of parameters. …”
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Calcium Extrusion Pump PMCA4: A New Player in Renal Calcium Handling? by Ellen P M van Loon, Robert Little, Sukhpal Prehar, René J M Bindels, Elizabeth J Cartwright, Joost G J Hoenderop

“…In the kidney, and more specifically, in the late distal convoluted tubule and connecting tubule, the fine-tuning of Ca2+ reabsorption from the pro-urine takes place. …”
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Caste, Constitution, Court, Equality: The Social Justice Imbroglio in Contemporary India by Ishita Banerjee-Dube

“…This article addresses these issues by revisiting the convoluted trajectory of positive discrimination (termed “reservation”) in India as an illustrative and instructive example. …”
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Raman micro-spectroscopy reveals the metabolic alterations in primary prostate tumor tissues of patients with metastases by Xiaoguang Shao, Bo Liu, Hongyang Qian, Qihan Zhang, Yinjie Zhu, Shupeng Liu, Heng Zhang, Jiahua Pan, Wei Xue

“…Subsequently, we used a convolutional neural network (CNN) structure to develop a classification model to predict metastatic PC based on the tissue Raman spectra, and then explored potential metabolic pathways via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. …”
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Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy by Lindsay A. Edwards, Christina Yang, Surbhi Sharma, Zih-Hua Chen, Lahari Gorantla, Sanika A. Joshi, Nicolas J. Longhi, Nahom Worku, Jamie S. Yang, Brandy Martinez Di Pietro, Saro Armenian, Aarti Bhat, William Border, Sujatha Buddhe, Nancy Blythe, Kayla Stratton, Kasey J. Leger, Wendy M. Leisenring, Lillian R. Meacham, Paul C. Nathan, Shanti Narasimhan, Ritu Sachdeva, Karim Sadak, Eric J. Chow, Patrick M. Boyle

“…We built DCNNs with four subsets of echocardiographic data differing in timing relative to case (survivor who developed cardiomyopathy) index diagnosis and two input formats (montages) with differing image selections. …”
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Towards prehospital risk stratification using deep learning for ECG interpretation in suspected acute coronary syndrome by Frederik M Zimmermann, Pim A L Tonino, Arjan Koks, Jesse P A Demandt, Marcel van ’t Veer, Pieter-Jan Vlaar, Thomas P Mast, Konrad A J van Beek, Marieke C V Bastiaansen

“…The aim of this study is to develop and validate a convolutional neural network (CNN)-based model for risk stratification of suspected NSTE-ACS patients and to compare its performance with currently available prehospital diagnostic tools.Methods For this study, an internal training cohort and an external validation cohort were used, both consisting of suspected NSTE-ACS patients. …”
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Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia by Ewunate Assaye Kassaw, Ewunate Assaye Kassaw, Ashenafi Kibret Sendekie, Ashenafi Kibret Sendekie, Bekele Mulat Enyew, Biruk Beletew Abate, Biruk Beletew Abate

“…Although the performance differences among the models were subtle (within a range of 0.001), the SVM classifier outperformed the others, achieving a recall of 0.9979 and an AUC of 0.9998. …”
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Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study by Seo Hyun Oh, Youngho Lee, Jeong-Heum Baek, Woongsang Sunwoo

“…Patients were stratified into colon and rectal cancer groups to account for biological and prognostic differences. Three models were developed and compared: a conventional artificial neural network (ANN), a basic convolutional neural network (CNN), and a transfer learning–based Visual Geometry Group (VGG)16 model. …”
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Instance Segmentation of Sugar Apple (<i>Annona squamosa</i>) in Natural Orchard Scenes Using an Improved YOLOv9-seg Model by Guanquan Zhu, Zihang Luo, Minyi Ye, Zewen Xie, Xiaolin Luo, Hanhong Hu, Yinglin Wang, Zhenyu Ke, Jiaguo Jiang, Wenlong Wang

“…An Efficient Multiscale Attention (EMA) mechanism was added to strengthen feature representation across scales, addressing sugar apple variability and maturity differences. Additionally, a Convolutional Block Attention Module (CBAM) refined the focus on key regions and deep semantic features. …”
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Fine-Grained Classification of Pressure Ulcers and Incontinence-Associated Dermatitis Using Multimodal Deep Learning: Algorithm Development and Validation Study by Alexander Brehmer, Constantin Seibold, Jan Egger, Khalid Majjouti, Michaela Tapp-Herrenbrück, Hannah Pinnekamp, Vanessa Priester, Michael Aleithe, Uli Fischer, Bernadette Hosters, Jens Kleesiek

“…We evaluated 4 models—2 convolutional neural networks and 2 transformer-based architectures—each with approximately 25 million parameters. …”
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Semantic Fusion-Oriented Bi-Typed Multi-Relational Heterogeneous Graph Neural Network by Yifan Sun, Jing Yan, Lilei Lu, Hongbo Zhang, Yanhong Shang

“…Additionally, it employs relational convolutions to capture relationship features within different types and fuses different relationship features through a relational-level attention mechanism. …”
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WCMU-net: An Effective Method for Reducing the Impact of Speckle Noise in SAR Image Change Detection by Yu Duan, Kaimin Sun, Wangbin Li, Jinjiang Wei, Song Gao, Yingjiao Tan, Wanghui Zhou, Jun Liu, Junyi Liu

“…For C-band SAR images, different satellite imaging and ground object reflections can produce varying levels of noise, necessitating a network design adaptable to different noise levels. …”
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Force output in giant-slalom skiing: A practical model of force application effectiveness. by Matt R Cross, Clément Delhaye, Jean-Benoit Morin, Maximilien Bowen, Nicolas Coulmy, Frédérique Hintzy, Pierre Samozino

“…While enhanced force production is considered key to high-level skiing, its relevance is convoluted. The aims of this study were to i) clarify the association between performance path length and velocity, ii) test the importance of radial force, and iii) explore the contribution of force magnitude and orientation to turn performance. …”
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Deep learning of the particulate and mineral-associated organic carbon fractions using a compositional transform and mid-infrared spectroscopy by Mingxi Zhang, Zefang Shen, Lewis Walden, Farid Sepanta, Zhongkui Luo, Lei Gao, Oscar Serrano, Raphael A. Viscarra Rossel

“…We used the centred log ratio (CLR) method to transform the data compositionally and then modelled POCmac, POCmic, POC (POCmac + POCmic), and MAOC with the spectra, using convolutional neural networks (CNN) and cubist for benchmarking. …”
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