Accuracy of Artificial Intelligence-Assisted Staging and Grading for Diagnosis of Periodontitis: A Cross-Sectional Study
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 47
- 试验地点
- 1
- 主要终点
- Establishment of an AI model for calculating periodontal bone loss (%) and assigning stage and grade of periodontitis using 2017 classification
研究概览
简要总结
This observational study aims to develop and assess the accuracy, specificity, and sensitivity of a deep learning model for the classification of periodontitis using panoramic radiographs and clinical data inputs. A total of 341 panoramic images will be retrospectively collected and labeled by experienced periodontists to train and test the model. The model will be evaluated for its ability to determine the stage and grade of periodontitis based on the 2017 classification guidelines set by the American Academy of Periodontology. The results will be compared to those of clinical experts to validate the AI-assisted diagnostic system. This study is conducted at the Faculty of Dentistry, Ain Shams University, in fulfillment of a Master's degree in Periodontology.
详细描述
a convolutional neural network (CNN)-based deep learning model will be trained using 341 panoramic radiographs and relevant clinical data to classify patients according to stage and grade of periodontitis. Images will be obtained from the Oral and Maxillofacial Radiology Department at Ain Shams University. The inclusion criteria includes radiographs of patients with periodontal bone loss and radiographs of patients with orthodontic brackets, mixed dentition, and artifacts will be excluded. Clinical data, including age, diabetes status, and smoking history, will be incorporated to calculate grading using the bone loss/age ratio of the testing set.
The collected dataset will be divided into 80% for training and 20% for testing. Six anatomical landmarks will be annotated per tooth to calculate the percentage of bone loss mesially and distally, which will be used to determine the stage of disease. Grading will be determined based on percentage bone loss relative to patient age and systemic modifiers. Expert-labeled datasets will serve as a reference standard for evaluating the performance of the AI model.
The primary objective is to evaluate the model's diagnostic accuracy for staging and grading compared to specialist assessments. The secondary objective is to measure the sensitivity and specificity of the model.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •patients with periodontitis causing radiographic bone loss
排除标准
- •x-ray images with
- •Mixed dentition
- •Orthodontic brackets
- •Images with artifacts and distortion
结局指标
主要结局
Establishment of an AI model for calculating periodontal bone loss (%) and assigning stage and grade of periodontitis using 2017 classification
时间窗: 10 months
Development of a machine learning model using retrospectively collected panoramic radiographs to calculate the percentage of periodontal bone loss (PBL) for each tooth and assign a stage (I-IV) and grade (A-C) of periodontitis according to the 2017 World Workshop classification of Periodontal and Peri-implant Diseases. The outcome will be reported as the accuracy percentage in which the model successfully provides both a PBL calculation and a corresponding stage and grade classification without processing errors.
次要结局
- Diagnostic performance of the AI model compared to specialist diagnosis (accuracy, sensitivity, specificity)(3 months)
研究者
Nariman Hesham Hamed Shaker
Master's Degree Candidate at the Department of Oral Medicine, Periodontology, and Oral Diagnosis
Ain Shams University