AI Model Detects Low Bone Mineral Density on Ankle and Foot X-Rays: A New Tool for Osteoporosis Screening
Table of Contents
In a groundbreaking study conducted by radiologists at MD Anderson Cancer Center in Houston, an AI model has been developed to detect low bone mineral density (BMD) on ankle and foot x-rays. This innovative approach could significantly enhance the screening process for osteoporosis, a condition that often goes undiagnosed despite its prevalence.
The Underutilization of DEXA Scans
Gold standard dual-energy x-ray absorptiometry (DEXA) scans are currently underused in osteoporosis screening. According to the study, while BMD testing is recommended for females aged 65 and older, males over 70, and younger individuals with risk factors, only 20 to 48% of at-risk patients are routinely screened using DEXA. This underutilization highlights a critical gap in healthcare that the new AI model aims to address.
Opportunistic Screening with AI
the researchers noted that millions of x-rays of the foot or ankle are obtained annually for evaluating fractures, arthropathy, or infection. These x-rays, obtained at different energies, are somewhat analogous to DEXA scans, which compare the relative attenuation of two different energy x-ray beams as they travel thru bone. This similarity inspired the creation of the first deep learning model to evaluate foot and ankle radiographs for osteoporosis and osteopenia.
study Methodology and Results
The study utilized a dataset of 907 patients over 50 years old who had undergone both DEXA scans and x-rays within 12 months. The dataset included 3,109 x-rays, with 80% used to train the model and 20% held separate for testing. Approximately 81% of patients had low BMD, and 18% did not.
The model’s performance was evaluated using several metrics,including area under the curve (AUC),sensitivity,specificity,accuracy,positive predictive value (PPV),and negative predictive value (NPV). The results were impressive:
- AUC: 87%
- Sensitivity: 89.9%
- Specificity: 83.6%
- accuracy: 89.9%
- PPV: 90.8%
- NPV: 74.1%
These metrics indicate that the AI model is highly effective in identifying patients with low BMD.
Practical Applications
In practice, the AI model could be used to opportunistically screen patients during routine x-ray examinations. Patients identified as having low BMD could then be referred for a formal DEXA scan and, if necessary, medical treatment.
The model’s versatility is notable, as it worked well using x-rays obtained from 24 different radiography manufacturers and models.This suggests that the model is likely to generalize well to other radiograph manufacturers and models, making it a valuable tool for widespread use.
Expanding the Scope of Osteoporosis Screening
The study adds to the emerging body of literature showing that radiographs of various regions, including the pelvis, hip, thoracic spine, lumbar spine, and chest, can be used for opportunistic osteoporosis screening. This broader approach could lead to earlier detection and treatment of osteoporosis, improving patient outcomes.
Conclusion
The full study, available here, provides detailed insights into the development and effectiveness of the AI model. As the first of its kind, this model represents a significant advancement in the use of AI for medical diagnostics and could revolutionize the way osteoporosis is screened and managed.
Key Points Summary
| Metric | Performance |
|————————-|————–|
| AUC | 87% |
| Sensitivity | 89.9% |
| Specificity | 83.6% |
| Accuracy | 89.9% |
| PPV | 90.8% |
| NPV | 74.1% |
This table summarizes the performance metrics of the AI model for predicting low or normal BMD on foot or ankle x-rays.
The integration of AI in medical diagnostics is poised to transform healthcare, making it more efficient and accessible.This study is a testament to the potential of AI in improving patient outcomes and enhancing the diagnostic process.
AI Model Detects Low bone Mineral Density on Ankle and Foot X-rays: A New Tool for Osteoporosis Screening
In a groundbreaking study conducted by radiologists at MD Anderson Cancer center in Houston, an AI model has been developed to detect low bone mineral density (BMD) on ankle and foot x-rays.This innovative approach could considerably enhance the screening process for osteoporosis, a condition that often goes undiagnosed despite its prevalence.
The Underutilization of DEXA Scans
Gold standard dual-energy x-ray absorptiometry (DEXA) scans are currently underused in osteoporosis screening. According to the study, while BMD testing is recommended for females aged 65 and older, males over 70, and younger individuals with risk factors, only 20 to 48% of at-risk patients are routinely screened using DEXA. This underutilization highlights a critical gap in healthcare that the new AI model aims to address.
Opportunistic Screening with AI
The researchers noted that millions of x-rays of the foot or ankle are obtained annually for evaluating fractures, arthropathy, or infection. These x-rays, obtained at different energies, are somewhat analogous to DEXA scans, which compare the relative attenuation of two different energy x-ray beams as thay travel through bone. This similarity inspired the creation of the first deep learning model to evaluate foot and ankle radiographs for osteoporosis and osteopenia.
Study Methodology and Results
the study utilized a dataset of 907 patients over 50 years old who had undergone both DEXA scans and x-rays within 12 months. The dataset included 3,109 x-rays, with 80% used to train the model and 20% held separate for testing. Approximately 81% of patients had low BMD, and 18% did not.
The model’s performance was evaluated using several metrics,including area under the curve (AUC),sensitivity,specificity,accuracy,positive predictive value (PPV),and negative predictive value (NPV). The results were extraordinary:
- AUC: 87%
- Sensitivity: 89.9%
- Specificity: 83.6%
- Accuracy: 89.9%
- PPV: 90.8%
- NPV: 74.1%
these metrics indicate that the AI model is highly effective in identifying patients with low BMD.
Practical Applications
In practice, the AI model could be used to opportunistically screen patients during routine x-ray examinations. Patients identified as having low BMD could then be referred for a formal DEXA scan and, if necessary, medical treatment.
The model’s versatility is notable, as it worked well using x-rays obtained from 24 different radiography manufacturers and models. This suggests that the model is likely to generalize well to other radiograph manufacturers and models, making it a valuable tool for widespread use.
Expanding the Scope of Osteoporosis Screening
The study adds to the emerging body of literature showing that radiographs of various regions, including the pelvis, hip, thoracic spine, lumbar spine, and chest, can be used for opportunistic osteoporosis screening. This broader approach could lead to earlier detection and treatment of osteoporosis, improving patient outcomes.
Conclusion
The full study, available here, provides detailed insights into the growth and effectiveness of the AI model. As the first of its kind, this model represents a significant advancement in the use of AI for medical diagnostics and could revolutionize the way osteoporosis is screened and managed.
Key Points Summary
| Metric | Performance |
|---|---|
| AUC | 87% |
| Sensitivity | 89.9% |
| Specificity | 83.6% |
| Accuracy | 89.9% |
| PPV | 90.8% |
| NPV | 74.1% |
This table summarizes the performance metrics of the AI model for predicting low or normal BMD on foot or ankle x-rays.
The integration of AI in medical diagnostics is poised to transform healthcare, making it more efficient and accessible.This study is a testament to the potential of AI in improving patient outcomes and enhancing the diagnostic process.
