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.
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Locations
-
-
Limburg
-
Maastricht, Limburg, Netherlands, 6229ER
- Maastricht University
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
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 Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
BS-UKA
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.
|
The aim is to investigate whether deep learning algorithms can detect bone metastasis with high accuracy and specificity.
|
|
BS-Namur
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.
|
The aim is to investigate whether deep learning algorithms can detect bone metastasis with high accuracy and specificity.
|
|
BS-Aalborg
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.
|
The aim is to investigate whether deep learning algorithms can detect bone metastasis with high accuracy and specificity.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The classification performance of DL algorithm compared to the ground truth
Time Frame: June 2021
|
Reporting the performance measures (Area under the curve, accuracy, specificity..etc)
|
June 2021
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Comparing the classification performance of the DL algorithm to that of physicians
Time Frame: 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
|
June 2021
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Publications and helpful links
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- MBDDL
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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