Deep Convolution Neural Network Based Artificial Intelligence Improves Diagnosis of Thyroid Scintigraphy for Thyrotoxicosis: a Dual Center Study

Abstract Background: 99mTc-pertechnetate thyroid scintigraphy is a valid avenue for distinguishing causes of thyrotoxicosis in the clinic, but the interpretation of thyroid scintigraphic images is subjected with significant variation among different inter-observers. We aim to develop an artificial intelligence (AI) system to improve the diagnosis of thyrotoxicosis.Materials and methods: We constructed an AI model based on a deep neural network with 2468 thyroid scintigraphic images collected from West China Hospital, and evaluated the diagnostic accuracy for classifying four patterns of thyrotoxicosis: ‘diffusely increased,’ ‘diffusely decreased,’ ‘focal increased,’ and ‘heterogeneous uptake.’ Then, we compared the diagnostic performance of the AI model and five physicians with 200 testing cohorts from two centers.Results: We constructed the AI model, which has the best performance in internal database validation based on four kinds of standout pre-trained networks. This AI model achieves satisfactory performance in classifying four patterns of thyrotoxicosis with an overall accuracy of 91.92% for internal and 86.75% for external data validation. In the following contrastive study, the AI model represented improved diagnostic accuracy and consistency than 5 physicians for interpreting data from West China Hospital (88% vs. 66~73%) and Panzhihua Central Hospital (83% vs. 53%~79%), respectively.Conclusion: Deep convolution neural network based AI model represented considerable performance in classifying four patterns of thyroid scintigraphic images; this may help physicians diagnose causes of thyrotoxicosis and reduced the physicians’ error rate.