The application effect of Generative Pre-Treatment Tool of Skeletal Pathology in functional lumbar spine radiographic analysis.
10.3760/cma.j.cn112139-20250212-00068
- Author:
Yilizati YILIHAMU
1
;
K ZHAO
1
;
H ZHONG
2
;
S Q FENG
1
Author Information
1. Orthopedic Research Center of Shandong University & Advanced Medical Research Institute, Department of Orthopedics, the Second Hospital of Shandong University, Jinan 250000,China.
2. Department of Radiology, the Second Hospital of Shandong University, Jinan 250000,China.
- Publication Type:Journal Article
- MeSH:
Humans;
Female;
Retrospective Studies;
Male;
Lumbar Vertebrae/diagnostic imaging*;
Middle Aged;
Aged;
Aged, 80 and over;
Artificial Intelligence;
Radiography;
Spondylolisthesis/diagnostic imaging*
- From:
Chinese Journal of Surgery
2025;63(10):936-941
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To investigate the application effectiveness of the artificial intelligence(AI) based Generative Pre-treatment tool of Skeletal Pathology (GPTSP) in measuring functional lumbar radiographic examinations. Methods: This is a retrospective case series study,reviewing the clinical and imaging data of 34 patients who underwent lumbar dynamic X-ray radiography at Department of Orthopedics, the Second Hospital of Shandong University from September 2021 to June 2023. Among the patients, 13 were male and 21 were female, with an age of (68.0±8.0) years (range:55 to 88 years). The AI model of the GPTSP system was built upon a multi-dimensional constrained loss function constructed based on the YOLOv8 model, incorporating Kullback-Leibler divergence to quantify the anatomical distribution deviation of lumbar intervertebral space detection boxes, along with the introduction of a global dynamic attention mechanism. It can identify lumbar vertebral body edge points and measure lumbar intervertebral space. Furthermore, spondylolisthesis index, lumbar index, and lumbar intervertebral angles were measured using three methods: manual measurement by doctors, predefined annotated measurement, and AI-assisted measurement. The consistency between the doctors and the AI model was analyzed through intra-class correlation coefficient (ICC) and Kappa coefficient. Results: AI-assisted physician measurement time was (1.5±0.1) seconds (range: 1.3 to 1.7 seconds), which was shorter than the manual measurement time ((2 064.4±108.2) seconds,range: 1 768.3 to 2 217.6 seconds) and the pre-defined annotation measurement time ((602.0±48.9) seconds,range: 503.9 to 694.4 seconds). Kappa values between physicians' diagnoses and AI model's diagnoses (based on GPTSP platform) for the lumbar slip index, lumbar index, and intervertebral angles measured by three methods were 0.95, 0.92, and 0.82 (all P<0.01), with ICC values consistently exceeding 0.90, indicating high consistency. Based on the doctor's manual measurement, compared with the predefined label measurement, altering AI assistance, doctors measurement with average annotation errors reduced from 2.52 mm (range: 0.01 to 6.78 mm) to 1.47 mm(range: 0 to 5.03 mm). Conclusions: The GPTSP system enhanced efficiency in functional lumbar analysis. AI model demonstrated high consistency in annotation and measurement results, showing strong potential to serve as a reliable clinical auxiliary tool.
