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Automated Detection of Metastatic Bone Disease on Bone Scintigraphy Scans

NCT05110430 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 2365

Last updated 2023-03-20

No results posted yet for this study

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

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.

Sponsors & Collaborators

  • Aalborg University Hospital

    collaborator OTHER

  • Centre Hospitalier Universitaire de Liege

    collaborator OTHER

  • University Hospital, Aachen

    collaborator OTHER

  • University of Namur

    collaborator OTHER

  • Maastricht University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-03-10
Primary Completion
2021-12-30
Completion
2021-12-31

Countries

  • Netherlands

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05110430 on ClinicalTrials.gov