Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs.

Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes.

Association of HLA-DQA1 and HLA-DQB1 gene polymorphisms with intracranial artery stenosis / 中华神经医学杂志

Objective To analyze the relationship between HLA- DQA1 and HLA-DQB1 gene polymorphism and intracranial artery stenosis in patients with ischemic stroke. Methods Fifteen ischemic stroke patients with concurrent severe intracranial artery stenosis, 49 ischemic stroke patients without stenosis and 52 healthy control subjects from Tianjin were investigated for HLA-DQA1 and HLA-DQB1 polymorphisms by polymerase chain reaction-sequence specific primers (PCR-SSP). Results Compared with the stroke patients without intracranial artery stenosis and the healthy controls, the stroke patients with artery stenosis showed significantly increased frequency of DQA1 *0501 and DQB1 *0501 alleles (P<0.05). Compared with the other 2 groups, the frequencies of DQA1 *0301 and DQB1 *0301 alleles in the stroke patients without intracranial artery stenosis were significantly increased (P<0.05). Conclusion HLA-DQA1 *0501 and HLA-DQB1*0501 alleles predispose to the genetic susceptibility of ischemic stroke with intracranial artery stenosis, and DQA1 *0301 and DQB1 *0301 alleles are associated with the susceptibility to ischemic cerebrovascular diseases.