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.
Study Type
OBSERVATIONAL
Enrollment
2,365
The aim is to investigate whether deep learning algorithms can detect bone metastasis with high accuracy and specificity.
Maastricht University
Maastricht, Limburg, Netherlands
The classification performance of DL algorithm compared to the ground truth
Reporting the performance measures (Area under the curve, accuracy, specificity..etc)
Time frame: June 2021
Comparing the classification performance of the DL algorithm to that of physicians
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
Time frame: June 2021
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