Convolutional neural networks for classifying three-phase bone scintigraphy after total knee arthroplasty: A pilot study

Objectives Total knee arthroplasty (TKA) is increasingly performed, with complications including pain, loosening, and periprosthetic joint infection. When the cause is unclear, three-phase bone scintigraphy (3PBS) is used to assess uptake patterns categorized as normal, aseptic, or septic involvement. We aimed to develop a CNN-based model that classifies phase-wise uptake patterns labeled by a nuclear medicine specialist; side-specific case-level categories were then assigned using a prespecified rule-based algorithm based on the phase outputs. Materials and Methods This retrospective single-center study included 102 patients (81 females, 21 males; mean age 67.0 ± 7.5 years, range 47–84) who underwent 3PBS at least 12 months after TKA (24 right, 25 left, 53 bilateral). Each scan included blood-flow, blood-pool, and delayed phase images labeled. Experiments using five CNN models (ResNet50, VGG11, VGG16, EfficientNet-B0, MobileNet) on phase- and laterality-specific inputs produced low accuracy (33–51%). Therefore, we developed customized CNNs for each phase–laterality combination. Predictions from these models were integrated using a decision algorithm for final side-specific case-level classification. Results The customized CNNs achieved 48/49 (98.0%) accuracy in the blood-flow phase, 47/48 (97.9%) in the blood-pool phase, and 50/52 (96.2%) in the delayed phase. Combining phase outputs yielded a side-specific case-level accuracy of 43/47 (91.5%) among test cases with complete three-phase data. Conclusion A phase-specific, laterality-aware CNN architecture showed high accuracy in classifying specialist-labeled 3PBS uptake patterns after TKA, with side-specific case-level categories assigned using a prespecified rule-based algorithm based on the phase outputs. These findings are promising but preliminary within this single-center pilot dataset.