Automated Detection of Metastatic Bone Disease on Bone Scintigraphy Scans
Completed
- Conditions
- Metastatic Bone Tumor
- Interventions
- Other: Deep learning based detection of metastatic bone disease on bone scintigraphy scans.
- Registration Number
- NCT05110430
- Lead Sponsor
- Maastricht University
- Brief Summary
Bone scintigraphy scans are two dimensional medical images that are used heavily in nuclear medicine. The scans detect changes in bone metabolism with high sensitivity, yet it lacks the specificity to underlying causes. Therefore, further imaging would be required to confirm the underlying cause. The aim of this study is to investigate whether deep learning can improve clinical decision based on bone scintigraphy scans.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 2365
Inclusion Criteria
- Patients who underwent a bone scintigraphy scan that is available with the radiologic report between 2010-2018
Exclusion Criteria
- The lack of a bone scan, or corresponding radiologic report
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description BS-UKA Deep learning based detection of metastatic bone disease on bone scintigraphy scans. Patients who underwent bone scintigraphy scanning between 2010 and 2018 at RTWH Aachen university hospital, and had a bone scan report that indicates the presence or absence of metastatic bone disease. BS-Namur Deep learning based detection of metastatic bone disease on bone scintigraphy scans. Patients who underwent bone scintigraphy scanning between 2010 and 2018 at Namur university hospital, and had a bone scan report that indicates the presence or absence of metastatic bone disease. BS-Aalborg Deep learning based detection of metastatic bone disease on bone scintigraphy scans. Patients who underwent bone scintigraphy scanning between 2010 and 2018 at Aalborg university hospital, and had a bone scan report that indicates the presence or absence of metastatic bone disease.
- Primary Outcome Measures
Name Time Method The classification performance of DL algorithm compared to the ground truth June 2021 Reporting the performance measures (Area under the curve, accuracy, specificity..etc)
- Secondary Outcome Measures
Name Time Method Comparing the classification performance of the DL algorithm to that of physicians June 2021 Correctness of the diagnosis of Dr versus AI (dichotomous variable: correct versus not correct) on a subset of the validation data, using a McNemar statistical test
Trial Locations
- Locations (1)
Maastricht University
🇳🇱Maastricht, Limburg, Netherlands