Investigative Radiology · 1994 · 38 citations · 0 references
MedicinePathologyRadiologyDiagnostic ImagingDiagnostic RadiologyImaging InformaticsBone PathologyComputer-aided DiagnosisMedical Image AnalysisMedical ImagingBone DiagnosisDeep Learning-based DiagnosisDeep Learning-based Lesion ClassificationLesion ClassificationNeural Network ModelingNeural Network-based DiagnosisTwo-layer Neural Computation
RATIONALE AND OBJECTIVES: Use of a neural network to diagnose focal lesions of bone was evaluated. METHODS: Imaging features of 709 lesions were encoded into a predetermined database. Data were divided into four groups and were analyzed using cross-validation by a two-layer feed-forward neural network. RESULTS: The lesions comprised 43 different pathologic diagnoses. Overall, the network was 85% accurate in distinguishing benign from malignant lesions. With a differential list of five diagnoses, the list was internally consistent regarding benign and malignant lesions 81.9% of the time. The network correctly diagnosed 56% of the lesions by pathologic diagnosis as its first choice. It included the correct diagnosis 71.8% of the time in a differential list of three diagnoses and 87.3% of the time in a differential list of nine diagnoses. CONCLUSION: Although not yet adequate for clinical use, neural network diagnosis of bone lesions is in its infancy and has important implications for the future analysis of focal bone lesions.