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Objectives: This study aimed to develop an automated deep learning (DL) system to detect and classify second molar (M2) pathologies associated with impacted third molars (ITMs) on panoramic radiographs. We sought to enha...

This study aimed to evaluate the performance of deep learning models for segmenting the augmented bone following transalveolar sinus floor elevation (TSFE). Cone-beam computed tomography (CBCT) data from 103 patients und...

Objectives: Deep learning networks have achieved significant progress in caries diagnosis, but automated localization and numbering of carious teeth in cone-beam computed tomography (CBCT) images remain underexplored. Th...

Background and Purpose: Deep learning segmentation (DLS) and planning (DLP) models in radiotherapy treatment planning increase consistency and reduce planning time. However, many DL segmentations and plans are manually a...

Manual Gross Tumor Volume (GTV) delineation for radiotherapy is time‑consuming and prone to inter‑observer variability. Deep Learning (DL)-based auto‑segmentation offers faster and more consistent solutions, but tr...

Power transformers are critical components of power systems, and their safe and reliable operation is essential for both industrial and residential applications. Real-time condition monitoring and early fault detection a...

Background and purpose: Accurate segmentation of the urethra is crucial for safe focal dose escalated radiotherapy, while prostate zone identification is important for prostate cancer diagnosis. Manual delineations on ma...

Rich in multiple strategic rare metals, deep-sea manganese nodules constitute an essential mineral resource for balancing terrestrial resource supply and demand and facilitating the global energy transition. Accurate pre...

With the rapid expansion of the digital music industry, streaming platforms are required to efficiently and accurately classify massive music collections to support personalized recommendation, content organization, and ...

Introduction: The advancement of AI-assisted oral disease diagnosis critically relies on the availability of annotated, high-quality datasets and resilient deep learning architectures. Nevertheless, progress in this doma...

Introduction and aims: Oral and maxillofacial surgery (OMFS) produces complex clinical data, while artificial intelligence (AI) applications in this field remain limited despite great potential. This study aimed to map t...