⚡ Quick Summary
This study explored the hand skeletal characteristics of pediatric patients with Turner syndrome (TS) and developed a deep learning model for disease screening. The model achieved an accuracy of 78.89%, significantly enhancing diagnostic precision.
🔍 Key Details
- 📊 Dataset: 101 pediatric patients with Turner syndrome
- 🧩 Features used: Hand X-ray parameters
- ⚙️ Technology: ResNet50 deep neural network
- 🏆 Performance: Accuracy 78.89%, Specificity 76.67%, Sensitivity 83.33%
🔑 Key Takeaways
- 🔍 Four key parameters were identified for diagnosing Turner syndrome: length ratio of metacarpal IV and III, distance between ulnoradial tangents, carpal angle, and ulnar-radial angle.
- 📏 Specificity reached 92.57% at a cutoff value of 0.40 cm for the distance between ulnoradial tangents.
- 📈 The ulnar-radius angle had a maximum Youden’s index at a cutoff of 170°, with a sensitivity of 66.34% and specificity of 61.38%.
- 🤖 Deep learning models significantly improved diagnostic accuracy for Turner syndrome.
- 🌟 This study highlights the potential of using radiographic parameters in conjunction with AI for better disease screening.
📚 Background
Turner syndrome is a genetic condition that affects females, often leading to short stature and various health issues. Unfortunately, misdiagnosis and missed diagnoses are common in clinical practice. This study aims to address these challenges by analyzing hand X-ray characteristics and leveraging deep learning technology to improve diagnostic accuracy.
🗒️ Study
Conducted as a retrospective case-control study, this research included 101 pediatric patients diagnosed with Turner syndrome. The study focused on analyzing their hand X-ray findings to identify specific skeletal characteristics that could serve as diagnostic indicators. The researchers also employed deep learning networks to create a predictive model for screening the disease.
📈 Results
The study identified four significant parameters that could aid in diagnosing Turner syndrome. The ResNet50 deep neural network architecture was utilized, yielding an impressive accuracy of 78.89%, specificity of 76.67%, and sensitivity of 83.33% on the test dataset. These results underscore the effectiveness of deep learning in enhancing diagnostic precision.
🌍 Impact and Implications
The findings from this study could have a profound impact on the early diagnosis of Turner syndrome. By integrating deep learning models with traditional radiographic analysis, healthcare professionals can achieve more accurate and timely diagnoses. This advancement not only improves patient outcomes but also paves the way for further research into AI applications in pediatric healthcare.
🔮 Conclusion
This study demonstrates the remarkable potential of combining deep learning with radiographic analysis for diagnosing Turner syndrome. The identification of key hand X-ray parameters as diagnostic indicators represents a significant step forward in clinical practice. As we continue to explore the integration of AI in healthcare, the future looks promising for improving diagnostic accuracy and patient care.
💬 Your comments
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Hand X-rays findings and a disease screening for Turner syndrome through deep learning model.
Abstract
BACKGROUND: Turner syndrome (TS) is one of the important causes of short stature in girls, but there are cases of misdiagnosis and missed diagnosis in clinical practice. Our aim is to analyze the hand skeletal characteristics of TS patients and establish a disease screening model using deep learning.
METHODS: A total of 101 pediatric patients with TS were included in this retrospective case-control study. Their radiation parameters from hand X-rays were summarized and compared. Receiver operating characteristic (ROC) curves for parameters with differences between the groups were plotted. Additionally, we used deep learning networks to establish a predictive model.
RESULTS: Four parameters were identified as having diagnostic value for TS: the length ratio of metacarpal IV and metacarpal III, the distance between ulnoradial tangents, the carpal angle, and the ulnar-radial angle. When the cutoff value of the distance between the ulnoradial tangents was 0.40 cm, the specificity reached 92.57%. And for the ulnar- radius angle, according to the ROC analysis, the maximum value of Youden’s index was obtained when the cut-off value was 170°, with a sensitivity of 66.34% and specificity of 61.38%. The ResNet50 deep neural network architecture was utilized, resulting in an accuracy of 78.89%, specificity of 76.67%, and sensitivity of 83.33% on a test dataset.
CONCLUSIONS: We propose that certain hand radiograph parameters have the potential to serve as diagnostic indicators for TS. The utilization of deep learning models has significantly enhanced the precision of disease diagnosis.
Author: [‘Wang Y’, ‘Wang Y’, ‘Hu F’, ‘Zhou L’, ‘Ding Y’, ‘Guo C’, ‘Chen Y’, ‘Hu Y’, ‘Liu S’, ‘Wang X’]
Journal: BMC Pediatr
Citation: Wang Y, et al. Hand X-rays findings and a disease screening for Turner syndrome through deep learning model. Hand X-rays findings and a disease screening for Turner syndrome through deep learning model. 2025; 25:177. doi: 10.1186/s12887-025-05532-9