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Automated Diagnosis and Classification of Temporomandibular Joint Degenerative Joint Disease via Artificial Intelligence Using CBCT Imaging.
J Dent
January 2025
Department of Oral & Maxillofacial Radiology, Peking University School & Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases, Beijing 100081, China; National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China; Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China. Electronic address:
Objectives: In this study, artificial intelligence techniques were used to achieve automated diagnosis and classification of temporomandibular joint (TMJ) degenerative joint disease (DJD) on cone beam computed tomography (CBCT) images.
Methods: An AI model utilizing the YOLOv10 algorithm was trained, validated and tested on 7357 annotated and corrected oblique sagittal TMJ images (3010 images of normal condyles and 4347 images of condyles with DJD) from 1018 patients who visited Peking University School and Hospital of Stomatology for temporomandibular disorders and underwent TMJ CBCT examinations. This model could identify DJD as well as the radiographic signs of DJD, namely, erosion, osteophytes, sclerosis and subchondral cysts.
Spatio-temporal transformers for decoding neural movement control.
J Neural Eng
January 2025
Department of Information Engineering, Electronics and Telecommunications, University of Rome La Sapienza, Piazzale Aldo Moro 5, Rome, 00185, ITALY.
Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to analyze neural activity in vivo remains a challenge, requiring a delicate balance between efficiency in low-data regimes and the interpretability of the results. Approach: To address this challenge, we introduce a novel specialized transformer architecture to analyze single-neuron spiking activity.
View Article and Find Full Text PDFA dual-domain network with division residual connection and feature fusion for CBCT scatter correction.
Phys Med Biol
January 2025
School of Biomedical Engineering, ShanghaiTech University, No. 1 Zhongke Road, Pudong New Area, Shanghai, Shanghai, 201210, CHINA.
Objective: This study aims to propose a dual-domain network that not only reduces scatter artifacts but also retains structure details in CBCT.
Approach: The proposed network comprises a projection-domain sub-network and an image-domain sub-network. The projection-domain sub-network utilizes a division residual network to amplify the difference between scatter signals and imaging signals, facilitating the learning of scatter signals.
A safe-enhanced fully closed-loop artificial pancreas controller based on deep reinforcement learning.
PLoS One
January 2025
Institute of Visual Informatics, The National University of Malaysia (UKM), Bangi, Malaysia.
Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enable adaptive insulin dosing control, they face numerous challenges, including safety and training efficiency, which have hindered their clinical application. This paper proposes a safe and efficient adaptive insulin delivery controller based on DRL.
View Article and Find Full Text PDFCorrection: Construction of a sustained-release hydrogel using gallic acid and lysozyme with antimicrobial properties for wound treatment.
Biomater Sci
January 2025
School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.
Correction for 'Construction of a sustained-release hydrogel using gallic acid and lysozyme with antimicrobial properties for wound treatment' by Wei Gong , , 2022, , 6836-6849, https://doi.org/10.1039/D2BM00658H.
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