Comparing acoustic representations for deep learning-based classification of underwater acoustic signals: A case study on orca (Orcinus orca) vocalizations by Fabio Frazao, Ruth Joy, Michael Dowd

“…The spectrogram is well-suited for many such pattern recognition algorithms, including those developed for computer vision, such as convolutional neural networks. …”
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Medical Data over Sound—CardiaWhisper Concept by Radovan Stojanović, Jovan Đurković, Mihailo Vukmirović, Blagoje Babić, Vesna Miranović, Andrej Škraba

“…Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. …”
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Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation by Simone Costantini, Anna Falivene, Mattia Chiappini, Giorgia Malerba, Carla Dei, Silvia Bellazzecca, Fabio A. Storm, Giuseppe Andreoni, Emilia Ambrosini, Emilia Biffi

“…Thus, quantitative assessment of engagement towards rehabilitation using physiological data and subjective evaluations is increasingly becoming vital. This study aimed at methodologically exploring the performance of artificial intelligence (AI) algorithms applied to structured datasets made of heart rate variability (HRV) and electrodermal activity (EDA) features to predict the level of patient engagement during RAGR. …”
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Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences by Eugenia Mylona, Dimitrios I. Zaridis, Charalampos Ν. Kalantzopoulos, Nikolaos S. Tachos, Daniele Regge, Nikolaos Papanikolaou, Manolis Tsiknakis, Kostas Marias, ProCAncer-I Consortium, Dimitrios I. Fotiadis

“…Their performance was evaluated in a nested cross-validation and externally, using seven performance metrics. …”
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A novel nomogram for survival prediction in renal cell carcinoma patients with brain metastases: an analysis of the SEER database by Fei Wang, Xihao Wang, Zhigang Feng, Jun Li, Hailiang Xu, Hengming Lu, Lianqu Wang, Zhihui Li

“…Potential risk factors were initially screened applying the eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning algorithms. Subsequently, multivariate COX regression analysis was performed to identify independent risk factors for constructing the predictive nomogram. …”
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Fault diagnosis method of mine hoist main bearing with small sample based on VAE-WGAN by Fan JIANG, Hongyan SONG, Xi SHEN, Zhencai ZHU, Shuman CHENG

“…At the data level, VAE-WGAN was tested using the Case Western Reserve University dataset, and hyperparameters were optimized by evaluating the generation ability of VAE-WGAN through quantitative indicators. …”
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How spatial resolution mediates canopy spectral diversity as a proxy for marsh plant diversity by Yi Fu, Yunlong Yao, Lei Wang, Huaihu Yi, Yuanqi Shan

“…Spectral reflectance variations comprehensively capture differences in the biochemical composition and morphological characteristics among plant species, making them a promising approach for monitoring and estimating plant diversity. …”
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Development of a Force Feedback Controller with a Speed Feedforward Compensator for a Cable-Driven Actuator by Juan Fang, Michael Haldimann, Bardia Amiryavari, Robert Riener

“…A PI force feedback controller was developed and evaluated in a static test. Subsequently, a dynamic test with a reference force of 100 N was conducted, during which the cable was pulled to move at different speeds. …”
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Functional Monitoring of Patients With Knee Osteoarthritis Based on Multidimensional Wearable Plantar Pressure Features: Cross-Sectional Study by Junan Xie, Shilin Li, Zhen Song, Lin Shu, Qing Zeng, Guozhi Huang, Yihuan Lin

“…Mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) were used as regression performance metrics to evaluate the results of different models. …”
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Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model by Fuyu Guo, Shiwei Sun, Xiaoqian Deng, Yue Wang, Wei Yao, Peng Yue, Shaoduo Wu, Junrong Yan, Junrong Yan, Junrong Yan, Xiaojun Zhang, Xiaojun Zhang, Xiaojun Zhang, Yangang Zhang, Yangang Zhang, Yangang Zhang

“…ObjectiveTo explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast cancer (BC). …”
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Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis by Ennio Idrobo-Avila, Gergo Bognar, Dagmar Krefting, Thomas Penzel, Peter Kovacs, Nicolai Spicher

“…<italic>Methods:</italic> We applied widely known algorithms entitled &#x201C;signal quality indicators&#x201D; to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10 s duration. …”
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AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

“…The MSCBs employ four parallel 1D convolutional layers with different kernel sizes, enabling the model to extract multi-scale features critical for learning patterns in complex environmental data. …”
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Diagnostic Value of F-FDG PET/CT Radiomics in Lymphoma: A Systematic Review and Meta-Analysis by Chaoying Liu MD, Jun Zhao PhD, Heng Zhang PhD, Xinye Ni PhD

“…This research aimed to systematically evaluate the diagnostic value of 18 F-FDG PET/CT radiomics in lymphoma by conducting a meta-analysis. …”
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The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

“…Moreover, challenges such as algorithmic bias, generalizability across diverse populations, and the necessity for clinician training must be carefully considered. …”
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Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach by Fetlework Gubena Arage, Zinabu Bekele Tadese, Eliyas Addisu Taye, Tigist Kifle Tsegaw, Tsegasilassie Gebremariam Abate, Eyob Akalewold Alemu

“…These algorithms were employed to predict cervical cancer screening uptake. …”
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Prominent events in the development of a simultaneous multidiagnostic system with synthetic peptides by Oscar Noya, Henry Bermúdez, Diana Pachón, Belkisyolé Alarcón de Noya, Diana Ortiz-Princz, Flor Helene Pujol, Sandra Losada

“…Specific epitopes with the greatest antigenic potential using immunoinformatic algorithms have been selected from worldwide and tropical pathogens and then assayed by a successive chain of immunological techniques [PEPSCAN®, enzyme-linked immunosorbent assay (ELISA), and MABA] to evaluate the sensitivity and specificity of those synthetic peptides for their usefulness in diagnosis. …”
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RehabHand&#x2014;A New Physical Rehabilitation Training Dataset: Construction and Benchmark Performances of the Relevant Hand Tasks by Sinh Huy Nguyen, Hoang Bach Nguyen, Thi Thu Hong Le, Chi Thanh Nguyen, Thi Thanh Huyen Nguyen, Quynh Tho Chu, Thi Lan Le, Thanh Hai Tran, Hai Vu

“…These labeled datasets are utilized to evaluate three primary tasks related to evaluating physical hand functions: including one- and two-stage neuronal networks for hand detection and identifying left or right patient&#x2019;s hand; hand tracking with SORT and DeepSORT algorithms. …”
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Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review by Umile Giuseppe Longo, Alberto Lalli, Guido Nicodemi, Matteo Giuseppe Pisani, Alessandro De Sire, Pieter D'Hooghe, Ara Nazarian, Jacob F. Oeding, Balint Zsidai, Kristian Samuelsson

“…Included were studies published between September 2018 and February 2024, which evaluated machine learning (ML) model effectiveness in improving orthopaedic imaging. …”
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Predicting 30-Day Venous Thromboembolism Following Total Joint Arthroplasty: Adjusting for Trends in Annual Length of Stay by Johnathan R. Lex, MBChB, MASc, Robert Koucheki, MD, MEng, Aazad Abbas, MD, Jesse I. Wolfstadt, MD, MSc, FRCSC, FAAOS, Alexander S. McLawhorn, MD, MBA, Bheeshma Ravi, MD, PhD, FRCSC

“…Predictive models (logistic regression, random forest, and XGBoost) were trained and tested based on year of surgery with different oversampling algorithms used to address data imbalance. …”
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Early detection of retinal and choroidal microvascular impairments in diabetic patients with myopia by Yufei Wu, Yufei Wu, Jiahui Jiang, Xiaoyu Deng, Xixi Zhang, Jinger Lu, Zian Xu, Yitian Zhao, Zai-Long Chi, Zai-Long Chi, Qinkang Lu, Qinkang Lu

“…PurposeTo evaluate and quantify diabetes-related retinal and choroid perfusion changes in individuals with and without high myopia and explore their associations with diabetes risk factors.MethodsDiabetic patients [n = 133; 43 without diabetic retinopathy in group DM; 48 non-proliferative diabetic retinopathies in group DR; 42 without DR but with high myopia in group HM] underwent ophthalmological and endocrinological examinations. …”
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