NCT05110430

Automated Detection of Metastatic Bone Disease on Bone Scintigraphy Scans

In Silico Clinical Trial Comparing the Reading Accuracy of Doctors and a Deep Learning Algorithm for Detection of Metastatic Bone Disease on Bone Scintigraphy Scans.

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 2,365 (actual)
• Sponsor: Maastricht University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

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.

Conditions

• Metastatic Bone Tumor

Interventions

Type	Name	Description
OTHER	Deep learning based detection of metastatic bone disease on bone scintigraphy scans.	The aim is to investigate whether deep learning algorithms can detect bone metastasis with high accuracy and specificity.

Timeline

• Start date: 2021-03-10
• Primary completion: 2021-12-30
• Completion: 2021-12-31
• First posted: 2021-11-08
• Last updated: 2023-03-20

Locations

1 site across 1 country: Netherlands