Oncologic patients underwent bone scans to evaluate bone metastasis. Artificial intelligence, deep learning, bone scan, bone scintigraphy, nuclear medicine, bone metastasis, diagnostic accuracy, convolutional neural network, retrospective study, computer-aided diagnosis
Conditions
Interventions
This group includes adult oncology patients who underwent bone scan imaging between 2021 and 2025 at the study institution. All included studies have complete anterior and posterior views with adequa
Diagnostic
Bone Scan Cohort
Sponsors
Phrapokkloa Hospital
Eligibility
Sex/Gender
All
Age
18 Years to No maximum
Inclusion criteria
Inclusion criteria: 1. Patients aged >18 years with oncologic indications for bone scan. 2. Complete anterior and posterior images of bone scan. 3. Adequate image quality. 4. Available reference standard including CT or MRI.
Exclusion criteria
Exclusion criteria: 1. Patients aged <18 years. 2. Non-oncologic indications. 3. Incomplete scans. 4. Amputation. 5. Unterfering devices. 6. Poor image quality. 7. Absence of reference standard.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Diagnostic performance At time of analysis Area under the receiver operating characteristic curve | — |
Secondary
| Measure | Time frame |
|---|---|
| Sensitivity At time of analysis Proportion of true positives among abnormal cases,Specificity At time of analysis Proportion of true negatives among normal cases,Accuracy At time of analysis Overall proportion of correctly classified cases Time point: | — |
Countries
Thailand
Contacts
Public ContactWasit Kanokwongnuwat
Phrapokkloa Hospital
Outcome results
None listed