A User-Friendly Machine Learning Pipeline for Automated Leaf Segmentation in by Michelle Lynn Yung, Kamila Murawska-Wlodarczyk, Alicja Babst-Kostecka, Raina Margaret Maier, Nirav Merchant, Aikseng Ooi

“…We quantitatively compare the fine-tuned Mask R-CNN model to Meta AI’s Segment Anything Model (SAM) and evaluate natural language prompts using Grounded SAM and the Leaf-Only SAM post-processing pipeline for refining segmentation outputs. …”
Get full text

Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000 by Yabin Zhang, Lei Yu, Yuting Lv, Tiantian Yang, Qi Guo

“…Results reveal exponential growth post-2017, driven by advancements in deep learning and multimodal data integration. …”
Get full text

Deep learning for real-time P-wave detection: A case study in Indonesia's earthquake early warning system by Adi Wibowo, Leni Sophia Heliani, Cecep Pratama, David Prambudi Sahara, Sri Widiyantoro, Dadan Ramdani, Mizan Bustanul Fuady Bisri, Ajat Sudrajat, Sidik Tri Wibowo, Satriawan Rasyid Purnama

“…We have integrated several post-processing techniques to further bolster the model's robustness against noise. …”
Get full text

Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…To identify the optimal model, both traditional machine learning and transfer learning approaches were employed, followed by model fusion using post-fusion techniques. The performance of the model was rigorously evaluated through the area under the curve (AUC), calibration curve analysis, and decision curve analysis (DCA). …”
Get full text

Advanced phenotyping in tomato fruit classification through artificial intelligence by Sandra Eulália Santos Faria, Alcinei Místico Azevedo, Nayany Gomes Rabelo, Varlen Zeferino Anastácio, Valentina de Melo Maciel, Deltimara Viana Matos, Elias Barbosa Rodrigues, Phelipe Souza Amorim, Janete Ramos da Silva, Fernanda de Souza Santos

“…Recent advances in the field of computational resources, such as image phenotyping have enabled pre- and post-harvest assessments that are both fast and precise. …”
Get full text

Evaluation and Early Detection of Downy Mildew of Lettuce Using Hyperspectral Imagery by Songtao Ban, Minglu Tian, Dong Hu, Mengyuan Xu, Tao Yuan, Xiuguo Zheng, Linyi Li, Shiwei Wei

“…The classification model using PLS, RF, and CNN successfully detected early physiological changes in lettuce within 24 h post-infection (highest accuracy = 0.764), offering an effective tool for early disease detection. …”
Get full text

DTC-m6Am: A Framework for Recognizing N6,2′-O-dimethyladenosine Sites in Unbalanced Classification Patterns Based on DenseNet and Attention Mechanisms by Hui Huang, Fenglin Zhou, Jianhua Jia, Huachun Zhang

“…The model then combines densely connected convolutional networks (DenseNet) and temporal convolutional network (TCN). …”
Get full text

Quantification of training‐induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females by Saied Ramedani, Ebru Kelesoglu, Norman Stutzig, Hendrik Von Tengg‐Kobligk, Keivan Daneshvar Ghorbani, Tobias Siebert

“…CG]) × 2 (TIME [pre‐intervention vs. post‐intervention]) analysis of variance (ANOVA) with repeated measures for the factor TIME. …”
Get full text

Automated Detection and Biomarker Identification Associated with the Structural and Functional Progression of Glaucoma on Longitudinal Color Fundus Images by Iyad Majid, Zubin Mishra, Ziyuan Chris Wang, Vikas Chopra, Dale Heuer, Zhihong Jewel Hu

“…Gradient-weighted class activation mapping (Grad-CAM) was employed for the post hoc visualization of image features, which may be associated with the objective POAG biomarkers (rather than the biomarkers determined by clinicians). …”
Get full text

Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature by Bosen Peng, Jiancheng Mu, Feng Xu, Wanyue Guo, Chuhuan Sun, Wei Fan

“…Notably, our analysis encompasses literature published beyond the year 2022, a pivotal year marking both the post-pandemic era and the rapid advancement of AI technologies. …”
Get full text

An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body r... by Yi Chen, David Pasquier, Damon Verstappen, Henry C. Woodruff, Philippe Lambin

“…Post-hoc explainability techniques were applied to elucidate the contribution of imaging data to predictive outcomes. …”
Get full text

Private Data Incrementalization: Data-Centric Model Development for Clinical Liver Segmentation by Stephanie Batista, Miguel Couceiro, Ricardo Filipe, Paulo Rachinhas, Jorge Isidoro, Inês Domingues

“…As the target of this study is not to propose a new image segmentation model, the existing medical imaging segmentation models—including U-Net, ResUNet++, Fully Convolutional Network, and a modified algorithm based on the Conditional Bernoulli Diffusion Model—are used. …”
Get full text

EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models by B. R. Nayana, M. N. Pavithra, S. Chaitra, T. N. Bhuvana Mohini, Thompson Stephan, Vijay Mohan, Neha Agarwal

“…Second, 1D Convolutional Neural Networks models are developed, and pre-processed EEG signals are fed as input. …”
Get full text

Fast Anomaly Detection for Vision-Based Industrial Inspection Using Cascades of Null Subspace PCA Detectors by Muhammad Bilal, Muhammad Shehzad Hanif

“…While recently several methods have been reported in the literature that have demonstrated impressive detection performance on standard benchmarks, they necessarily rely on computationally intensive CNN architectures and post-processing techniques, necessitating access to high-end GPU hardware and limiting practical deployment in resource-constrained settings. …”
Get full text

Applying SSVEP BCI on Dynamic Background by Junkai Li, Boxun Fu, Fu Li, Wenkai Gu, Youshuo Ji, Yang Li, Tiejun Liu, Guangming Shi

“…Furthermore, we proposed Multi-scale Temporal-Spatial Global average pooling Neural Network (MTSGNN), an end-to-end network for decoding SSVEP signals evoked by the post-modulation paradigm. MTSGNN is built with efficient convolutional structures and uses global average pooling to achieve classification, which effectively reduces the risk of model overfitting on small EEG datasets and improves classification performance. …”
Get full text

Nozzle Jamming Granularized Blood‐Derived Proteins for Bioprinting Cell‐Instructive Architectures by Lucas S. Ribeiro, João Rocha Maia, Vítor M. Gaspar, Catarina A. Custódio, Emerson R. Camargo, Rita Sobreiro‐Almeida, João F. Mano

“…Printability was evaluated in filaments, scaffold grids, and convoluted structures. Taking advantage of the previously introduced photocurable moieties, post‐printing photocrosslinking was used for the annealing of the microgels, leading to increased scaffold mechanical stability and robustness. …”
Get full text

A Fog Computing-Based Cost-Effective Smart Health Monitoring Device for Infectious Disease Applications by Saranya Govindakumar, Vijayalakshmi Sankaran, Paramasivam Alagumariappan, Bhaskar Kosuru Bojji Raju, Daniel Ford

“…Wearable technology offers a promising remedy to this persistent issue by offering continuous respiratory parameter monitoring, facilitating the early control and intervention of post-COVID-19 issues with respiration. In an effort to enhance patient outcomes and reduce expenses related to healthcare, this paper examines the possibility of using wearable technology to provide remote surveillance and the early diagnosis of respiratory problems in individuals suffering from COVID-19. …”
Get full text

Clinical and genetic characteristics of type 3 lissencephaly caused by a mutation in the TUBA1A gene (OMIM: 611603) by D. M. Guseva, T. V. Markova, L. A. Bessonova, S. S. Nikitin, E. L. Dadali, O. A. Shchagina

“…Lissencephaly (LIS) is a spectrum of malformations of the cerebral cortex that occur as a result of impaired migration of neuronal precursors to the cortical plate and the formation of furrows and convolutions in the post‑migration period of embryonic development. …”
Get full text

Can AI-Based ChatGPT Models Accurately Analyze Hand–Wrist Radiographs? A Comparative Study by Ahmet Yıldırım, Orhan Cicek, Yavuz Selim Genç

“…<b>Methods</b>: This study evaluated the performance of three ChatGPT-based models (GPT-4o, GPT-o4-mini-high, and GPT-o1-pro) in predicting bone age and growth stage using 90 anonymized hand–wrist radiographs (30 from each growth stage—pre-peak, peak, and post-peak—with equal male and female distribution). …”
Get full text

COMPARISON OF POROSITY PREDICTION FROM SEISMIC DATA IN THE F3 BLOCK, NETHERLANDS USING MACHINE LEARNING by Urip Nurwijayanto Prabowo, Sudarmaji Sudarmaji, Jarot Setyowiyoto, Sismanto Sismanto

“…The data from the F3 block consists of six post-stack seismic lines with an inline spacing of 40 meters and three wells. …”
Get full text