ArticleDiagnostics (Basel, Switzerland)2023
Artificial Intelligence of Object Detection in Skeletal Scintigraphy for Automatic Detection and Annotation of Bone Metastases.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Clinical Potential of Artificial Bone Scintigraphy from Early-Phase Bone Scintigraphy Using Unpaired Image-to-Image Translation in Patients with Breast Cancer: A Single-Center Prospective Study.Tomography (Ann Arbor, Mich.) · 2026Article
- Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
- Artificial Intelligence in bone Metastases: A systematic review in guideline adherence of 92 studies.Journal of bone oncology · 2025Review
- Automatic detecting multiple bone metastases in breast cancer using deep learning based on low-resolution bone scan images.Scientific reports · 2025Article
- Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review.Cancers · 2024Review
- Article
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7 authors.
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Abstract
backgroundWhen cancer has metastasized to bone, doctors must identify the site of the metastases for treatment. In radiation therapy, damage to healthy areas or missing areas requiring treatment should be avoided. Therefore, it is necessary to locate the precise bone metastasis area. The bone scan is a commonly applied diagnostic tool for this purpose. However, its accuracy is limited by the nonspecific character of radiopharmaceutical accumulation. The study evaluated object detection techniques to improve the efficacy of bone metastases detection on bone scans.
methodsWe retrospectively examined the data of 920 patients, aged 23 to 95 years, who underwent bone scans between May 2009 and December 2019. The bone scan images were examined using an object detection algorithm.
resultsAfter reviewing the image reports written by physicians, nursing staff members annotated the bone metastasis sites as ground truths for training. Each set of bone scans contained anterior and posterior images with resolutions of 1024 × 256 pixels. The optimal dice similarity coefficient (DSC) in our study was 0.6640, which differs by 0.04 relative to the optimal DSC of different physicians (0.7040).
conclusionsObject detection can help physicians to efficiently notice bone metastases, decrease physician workload, and improve patient care.
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