CART-ANOVA-Based Transfer Learning Approach for Seven Distinct Tumor Classification Schemes with Generalization Capability by Shiraz Afzal, Muhammad Rauf, Shahzad Ashraf, Shahrin Bin Md Ayob, Zeeshan Ahmad Arfeen

“…<b>Background/Objectives:</b> Deep transfer learning, leveraging convolutional neural networks (CNNs), has become a pivotal tool for brain tumor detection. …”
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Introduction to deep learning methods for multi‐species predictions by Yuqing Hu, Sara Si‐Moussi, Wilfried Thuiller

“…Specifically, we introduced four distinct deep learning models that use site × species community data but differ in their internal structure or on the input environmental data structure: (1) a multi‐layer perceptron (MLP) model for tabular data (e.g. in‐situ/raster climate or soil data), (2) a convolutional neural network (CNN) and (3) a vision transformer (ViT) models tailored for image data (e.g. aerial ortho‐photographs, satellite imagery), and a multimodal model that integrates both tabular and image data. …”
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Transferring Learned ECG Representations for Deep Neural Network Classification of Atrial Fibrillation with Photoplethysmography by Jayroop Ramesh, Zahra Solatidehkordi, Raafat Aburukba, Assim Sagahyroon, Fadi Aloul

“…However, the scarcity of large-scale public PPG datasets acquired from wearable devices hinders the development of intelligent automatic AF detection algorithms unaffected by motion artifacts, saturated ambient noise, inter- and intra-subject differences, or limited training data. In this work, we present a deep learning framework that leverages convolutional layers with a bidirectional long short-term memory (CNN-BiLSTM) network and an attention mechanism for effectively classifying raw AF rhythms from normal sinus rhythms (NSR). …”
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Total lesion glycolysis of primary tumor and lymphnodes is a strong predictor for development of distant metastases in oropharyngeal carcinoma patients with independent validation... by Sebastian Zschaeck, Marina Hajiyianni, Patrick Hausmann, Pavel Nikulin, Emily Kukuk, Christian Furth, Paulina Cegla, Elia Lombardo, Joanna Kazmierska, Adrien Holzgreve, Iosif Strouthos, Carmen Stromberger, Claus Belka, Michael Baumann, Mechthild Krause, Guillaume Landry, Witold Cholewinski, Jorg Kotzerke, Daniel Zips, Jörg van den Hoff, Frank Hofheinz

“…Additional analyses with manually delineated contours of the validation cohort revealed a similar effect (HR = 3.47, p = 0.026). No considerable differences between HPV positive and negative disease were observed. …”
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3D cloud masking across a broad swath using multi-angle polarimetry and deep learning by S. R. Foley, S. R. Foley, K. D. Knobelspiesse, A. M. Sayer, A. M. Sayer, M. Gao, M. Gao, J. Hays, J. Hoffman

“…However, multi-angle sensor configurations contain implicit information about 3D structure, due to parallax and atmospheric path differences. Extracting that implicit information requires computationally expensive radiative transfer techniques. …”
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Brain tau PET-based identification and characterization of subpopulations in patients with Alzheimer’s disease using deep learning-derived saliency maps by Yanxiao Li, Xiuying Wang, Qi Ge, Manuel B Graeber, Shaozhen Yan, Jian Li, Shuyu Li, Wenjian Gu, Shuo Hu, Tammie L. S. Benzinger, Jie Lu, Yun Zhou

“…A three dimensional-convolutional neural network model was employed for AD detection using standardized uptake value ratio (SUVR) images. …”
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Image-based honey bee larval viral and bacterial diagnosis using machine learning by Duan C. Copeland, Brendon M. Mott, Oliver L. Kortenkamp, Robert J. Erickson, Nathan O. Allen, Kirk E. Anderson

“…Correct field diagnosis of brood disease is challenging and requires years of experience to identify and differentiate various disease states according to subtle differences in larval symptomology. To explore the feasibility of an image-based AI diagnosis tool, we collaborated with apiary inspectors and researchers to generate a dataset of 2,759 honey bee larvae images from Michigan apiaries, molecularly verified through 16 S rRNA microbiome sequencing and qPCR viral screening. …”
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Non-Contact Oxygen Saturation Estimation Using Deep Learning Ensemble Models and Bayesian Optimization by Andrés Escobedo-Gordillo, Jorge Brieva, Ernesto Moya-Albor

“…On the other hand, regarding Bland–Altman analysis, the upper and lower limits of agreement for the Mean of Differences (MOD) between the estimation and the ground truth were 1.04 and −1.05, with an MOD (bias) of −0.00175; therefore, MOD <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>±</mo><mspace width="3.33333pt"></mspace><mn>1.96</mn><mi>σ</mi></mrow></semantics></math></inline-formula> = −0.00175 ± 1.04. …”
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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

“…While recent advances in deep learning enable modeling of temporal patterns in glucose fluctuations, most of the existing methods rely on unimodal inputs and fail to account for individual physiological differences that influence interstitial glucose dynamics. …”
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Adaptation of the microflora of pig farms to the disinfectant “Sviteco PIP Multi” and the antagonistic activity of the probiotic bacilli contained in its composition by V. O. Myronchuk, R. A. Peleno

“…The antagonistic activity of the probiotic components of the disinfectant “Sviteco PIP Multi” against coccal and tortuous isolates did not differ significantly, and the conditioned dew retention zones ranged from 14.2 to 23 mm.…”
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Exploiting the power of stepwise intraoperative irrigant activation to maximize oval canal disinfection: an ex-vivo investigation by Mohammed Turky, Shaimaa Hamdy, Soha Elhady

“…The combination of SIA and CUI achieved the greatest bacterial reduction (1.85 ± 0.99), followed by the CUI (3.00 ± 0.13), SIA (3.54 ± 0.26), and CSI (4.29 ± 0.16) groups, respectively, with significant differences between them (p <.05). Conclusions Stepwise intraoperative irrigant activation was able to reduce the bacterial load in the oval root canals significantly compared to the basic chemo-mechanical preparation, with maximal disinfection achieved when combined with conventional ultrasonic irrigation. …”
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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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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

“…The SHAP values reflected the compositional modelling and identified important organic and inorganic functional groups that differed by fraction and land use. Our approach can complement conventional physical SOC fractionations and improve the cost-effectiveness of the measurements, especially when there are many samples to measure, thus enhancing our understanding of SOC dynamics.…”
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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

“…The objective of this study was to investigate the metabolic differences in the primary tumor tissues between localized PC and metastatic PC using Raman micro-spectroscopy and metabolomics analysis, and then explore potential biomarkers for predicting metastasis and the potential metabolic pathways during the progression from localized prostate cancer to metastasis. …”
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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

“…However, these studies were aimed at a study population with a high prevalence of occlusive myocardial infarction, which could explain the differing levels of diagnostic performance.Conclusion Integrating AI in prehospital ECG interpretation improves the identification of patients at low risk for having NSTE-ACS. …”
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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

“… Abstract BackgroundPressure ulcers (PUs) and incontinence-associated dermatitis (IAD) are prevalent conditions in clinical settings, posing significant challenges due to their similar presentations but differing treatment needs. Accurate differentiation between PUs and IAD is essential for appropriate patient care, yet it remains a burden for nursing staff and wound care experts. …”
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