Deep learning for intelligent diagnosis in thyroid scintigraphy

Abstract

Objective To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy. Methods We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model’s performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents. Results The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided “diagnostic assistance” to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents. Conclusion DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves’ disease and subacute thyroiditis.

Diagnosis of thyroid disease using deep convolutional neural network models applied to thyroid scintigraphy images: a multicenter study

“…Qiao et al. ( 34 ) reported good diagnostic performance of deep learning models (i.e., AlexNet, VGG-16, and ResNet) in thyroid scintigraphy. Ma et al.…”

A narrative review of deep learning in thyroid imaging: current progress and future prospects

“…Other studies have used thyroid scintigraphy to construct DL models. DL-based models might also serve as tools in the diagnosis of Graves disease and subacute thyroiditis ( 107 ). A modified DenseNet architecture was tested for categorizing Graves disease, Hashimoto thyroiditis, and subacute thyroiditis ( 51 ).…”

Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review

“…A total of 1643 articles were extrapolated with the computer literature search and, by reviewing the titles and abstracts, 1627 of them were excluded because the reported data were not within the field of interest of this review. Sixteen articles were therefore selected and retrieved in full-text version [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] and one additional study was also found screening the references of these articles (Fig. 1) [34]; as a consequence, the total number of studies evaluated in the review was 17.…”

“…Ten studies focused on PET imaging [18][19][20][21][22][23][24][25][26]34], 8 with PET/computed tomography (PET/CT) hybrid tomographs [19-20, 22-26, 34] and 2 with both PET/CT and PET [18,21]. Furthermore, 7 studies focused on single photon imaging [27][28][29][30][31][32][33] and in particular 3 were performed with single photon emission computed tomography (SPECT) [27,29,31] while 4 were performed with planar scintigraphic scans [28,30,[32][33].…”