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Deep learning-based detection of incisal translucency patterns.
J Prosthet Dent
January 2025
Principal, Dr. Ambedkar Institute of Technology, Bangalore, India.
Statement Of Problem: The evaluation of incisal translucency in anterior teeth greatly influences esthetic treatment outcomes. This evaluation is mostly subjective and often overlooked among dental professionals. The application of artificial intelligence-based models to detect the incisal translucency of anterior teeth may be of value to dentists in their restorative dental practice, but studies are lacking.
View Article and Find Full Text PDFCurrent availability of radiotherapy devices in Peru and artificial intelligence-based analysis for constructing a nationwide implementation plan.
Radiother Oncol
January 2025
Department of Radiotherapy, Instituto Nacional de Enfermedades Neoplasicas, Lima, Peru; Department of Radiation Oncology, Oncosalud - Auna, Lima, Peru. Electronic address:
Purpose: We provide for the first time a comprehensive situational diagnosis and propose an artificial intelligence (AI)-assisted nationwide plan of implementation, attending the most urgent needs.
Methods: Baseline information was collected from open-source databases of the Peruvian Government. Data on cancer incidence from the Health Authorities and GLOBOCAN were collected and compared.
Automatic Vertical Root Fracture Detection on Intraoral Periapical Radiographs With Artificial Intelligence-Based Image Enhancement.
Dent Traumatol
January 2025
Department of Endodontology, Maurice and Gabriela Goldschleger School of Dental Medicine, Tel Aviv University, Tel Aviv, Israel.
Background/aim: To explore transfer learning (TL) techniques for enhancing vertical root fracture (VRF) diagnosis accuracy and to assess the impact of artificial intelligence (AI) on image enhancement for VRF detection on both extracted teeth images and intraoral images taken from patients.
Materials And Methods: A dataset of 378 intraoral periapical radiographs comprising 195 teeth with fractures and 183 teeth without fractures serving as controls was included. DenseNet, ConvNext, Inception121, and MobileNetV2 were employed with model fusion.
Residual-attention deep learning model for atrial fibrillation detection from Holter recordings.
J Electrocardiol
January 2025
Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy. Electronic address:
Background: Detecting subtle patterns of atrial fibrillation (AF) and irregularities in Holter recordings is intricate and unscalable if done manually. Artificial intelligence-based techniques can be beneficial. In fact, with the rapid advancement of AI, deep learning (DL) demonstrated the capability to identify AF from ECGs with significant performance.
View Article and Find Full Text PDFInfluence of edentulous areas on the accuracy of the maximum intercuspal position recorded by using different intraoral scanners or an artificial intelligence-based program.
J Prosthet Dent
January 2025
Associate Professor, Department of Dental Medicine, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain.
Statement Of Problem: Intraoral scans can be articulated in maximum intercuspal position (MIP) by using an artificial intelligence (AI) based program; however, the impact of edentulous areas on the accuracy of the MIP located using this AI-based program is unknown.
Purpose: The purpose of this in vitro study was to assess the impact of edentulous areas (0, 1, 2, 3, and 4 posterior mandibular teeth) on the accuracy of the MIP located using 3 intraoral scanners (IOSs) and an AI-based program.
Material And Methods: Stone casts articulated in MIP in an articulator were digitized (T710).
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