Two-stage deep learning framework for magnetic resonance imaging-based detection of temporomandibular joint osteoarthritis

Deep learning diagnosis of TMJ osteoarthritis using MRI ensembles and CBCT–MRI fusion

We compared single-modality models, intra-modality ensembles, and selective cone-beam computed tomography (CBCT)–magnetic resonance imaging (MRI) fusion for diagnosing temporomandibular joint osteoarthritis (TMJ-OA). This single-center retrospective cohort included 1598 patients (1118 females and 480 males; mean age ± ...

Deep Learning-Based Classification of Bone Lesions on CT Scans of Metastatic Spine Disease Patients: A 3D-Convolutional Neural Network Approach

Deep Learning Applications in Cone-Beam Computed Tomography (CBCT) for Automatic Detection and Segmentation of Periapical Lesions: A Comprehensive Clinical and Radiographic Evaluation

Proper diagnosis and segmentation of periapical lesions using Cone Beam Computed Tomography (CBCT) imaging play an important role in determining the best possible strategy for treatment of such pathologies. Even though CBCT provides a better three-dimensional visualization than regular two-dimensional X-ray images, mul...

Multimodal transformer-enhanced deep learning for accurate assessment of mandibular third molar–inferior alveolar canal contact using panoramic radiography and CBCT slices

A dual-stream, transformer-enhanced multimodal (panoramic radiography + selected CBCT slices) deep learning framework designed as a clinician-in-the-loop system aimed at reducing diagnostic variability in complex boundary cases where visual interpretation alone may be subjective.

Diagnostic Accuracy of Deep Learning for Detection of Incipient and Advanced Radiographic Interproximal Caries in Mixed Dentition Periapical Radiographs.

OBJECTIVES This study aimed to develop and compare deep-learning models for detecting proximal carious lesions of different radiographic severity on mixed-dentition periapical radiographs. METHODS A retrospective diagnostic accuracy study included 1,838 digital periapical radiographs from pediatric patients in the mi...

Deep Learning Method Based on Multimodal Medical Image Fusion for Predicting Acute Vertebral Fracture.

RATIONALE AND OBJECTIVES The study aimed to develop and validate a deep learning (DL) model based on X-ray and computed tomography (CT) to diagnose acute vertebral fractures (VFs), and to compare its diagnostic accuracy against spine surgeons. MATERIALS AND METHODS This single-center, retrospective diagnostic accurac...