An intelligent platform for ultrasound diagnosis of thyroid nodules

Abstract

This paper proposed a non-segmentation radiological method for classification of benign and malignant thyroid tumors using B mode ultrasound data. This method aimed to combine the advantages of morphological information provided by ultrasound and convolutional neural networks in automatic feature extraction and accurate classification. Compared with the traditional feature extraction method, this method directly extracted features from the data set without the need for segmentation and manual operations. 861 benign nodule images and 740 malignant nodule images were collected for training data. A deep convolution neural network VGG-16 was constructed to analyze test data including 100 malignant nodule images and 109 benign nodule images. A nine fold cross validation was performed for training and testing of the classifier. The results showed that the method had an accuracy of 86.12%, a sensitivity of 87%, and a specificity of 85.32%. This computeraided method demonstrated comparable diagnostic performance with the result reported by an experienced radiologist based on American college of radiology thyroid imaging reporting and data system (ACR TI-RADS) (accuracy: 87.56%, sensitivity: 92%, and specificity: 83.49%). The automation advantage of this method suggested application potential in computer-aided diagnosis of thyroid cancer.

Artificial Intelligence for Thyroid Nodule Characterization: Where Are We Standing?

“…All the evaluated studies showed significant high overall diagnostic accuracy of CNNs, above 90%, which does not differ much from that of expert radiologists. In particular, most of the studies demonstrate a comparable diagnostic accuracy, such as Watkins et al, Bai et al, Ye et al, Koh et al, and Fresilli et al [ 4 , 16 , 20 , 30 , 40 ]. Approximately the same number of studies demonstrate a higher diagnostic accuracy of AI systems compared to that of expert radiologists (e.g., Sun et al, Peng et al, and Zhou et al) [ 15 , 22 , 23 ], or vice versa, a superiority of diagnostic accuracy by expert radiologists compared to that of AI systems (e.g., Zhang et al and Han et al) [ 32 , 33 ].…”

“…The search identified 166 studies from January 2012 to April 2022; of these, 63 were further considered. After a full text read, the final studies included in the review were 30 in number; they are all listed below in Table 1 [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ].…”

Clinical workflow of sonographers performing fetal anomaly ultrasound scans: deep‐learning‐based analysis

“…The lack of universal scanning sequence patterns supports the somewhat opportunistic nature of anomaly scanning. Unlike non-obstetric ultrasound, obstetric ultrasound scans are restricted by fetal position and movement 22,23 . It is likely that while performing anomaly scans, sonographers have a preferred scanning sequence, and at the same time, take advantage of the fetal position, capturing structures that are more favorably visualized in each part of the scan.…”

Automatic Thyroid Ultrasound Image Classification Using Feature Fusion Network

“…In recent years, application of artificial intelligence technology in the medicine has gradually increased, especially in imaging [3][4][5] and signal [6]. How to use information of ultrasound images to establish a computerassisted automated thyroid diagnosis system is an important direction of current research [7,8].…”