Prediction of suspicious thyroid nodule using artificial neural network based on radiofrequency ultrasound and conventional ultrasound: A preliminary study

Liu, Chunrui; Xie, Linzhou; Kong, Wencheng; Lü, Xiaoling; Zhang, Dong; Wu, Min; Zhang, Lijuan; Yang, Bin

doi:10.1016/j.ultras.2019.105951

Cited by 20 publications

(13 citation statements)

References 22 publications

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“…AI techniques for US-based diagnosis of thyroid nodules can be divided into two parts: recognition (or segmentation) and classification. In recent years, various advanced algorithms have been proposed to improve the diagnostic accuracy, including support vector machines (21), random forest learning algorithm (22), and artificial neural network algorithm (23), among others. The development of deep learning is another great advancement, which has led to faster, more accurate, and fully automatic diagnoses.…”

Section: Performance Of the Cad System For Us Diagnosis Of Thyroid Nodulesmentioning

confidence: 99%

A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules

et al. 2021

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PurposeThe fully automatic AI-Sonic computer-aided design (CAD) system was employed for the detection and diagnosis of benign and malignant thyroid nodules. The aim of this study was to investigate the efficiency of the AI-Sonic CAD system with the use of a deep learning algorithm to improve the diagnostic accuracy of ultrasound-guided fine-needle aspiration (FNA).MethodsA total of 138 thyroid nodules were collected from 124 patients and diagnosed by an expert, a novice, and the Thyroid Imaging Reporting and Data System (TI-RADS). Diagnostic efficiency and feasibility were compared among the expert, novice, and CAD system. The application of the CAD system to enhance the diagnostic efficiency of novices was assessed. Moreover, with the experience of the expert as the gold standard, the values of features detected by the CAD system were also analyzed. The efficiency of FNA was compared among the expert, novice, and CAD system to determine whether the CAD system is helpful for the management of FNA.ResultIn total, 56 malignant and 82 benign thyroid nodules were collected from the 124 patients (mean age, 46.4 ± 12.1 years; range, 12–70 years). The diagnostic area under the curve of the CAD system, expert, and novice were 0.919, 0.891, and 0.877, respectively (p < 0.05). In regard to feature detection, there was no significant differences in the margin and composition between the benign and malignant nodules (p > 0.05), while echogenicity and the existence of echogenic foci were of great significance (p < 0.05). For the recommendation of FNA, the results showed that the CAD system had better performance than the expert and novice (p < 0.05).ConclusionsPrecise diagnosis and recommendation of FNA are continuing hot topics for thyroid nodules. The CAD system based on deep learning had better accuracy and feasibility for the diagnosis of thyroid nodules, and was useful to avoid unnecessary FNA. The CAD system is potentially an effective auxiliary approach for diagnosis and asymptomatic screening, especially in developing areas.

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Section: Performance Of the Cad System For Us Diagnosis Of Thyroid Nodulesmentioning

confidence: 99%

A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules

et al. 2021

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“…The research object was not thyroid nodules in 2 articles from database retrieval and 1 article from manual journal retrieval were also excluded. Finally, 11 articles (28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38) were included in this meta-analysis. Figure 1 shows a flow chart of literature retrieval process.…”

Section: Search Results and Basic Information Of The Documentsmentioning

confidence: 99%

“…A total of 5 included articles (31)(32)(33)(34)38) analyzed the diagnostic results of conventional ultrasound. Figure 6 shows a forest plot of the sensitivity and specificity of individual and aggregate studies of conventional ultrasound.…”

Section: Meta-analysis Of Conventional Ultrasound Diagnosismentioning

confidence: 99%

Systematic review and meta-analysis: diagnostic value of different ultrasound for benign and malignant thyroid nodules

Wang¹,

Zhou²,

Shen³

et al. 2022

Gland Surg

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“…Conventional ultrasound has been recognized as a standard for the evaluation of benign and malignant thyroid nodules. In general, color Doppler blood flow characteristics are not considered, and solid nodules with clear boundary, regular shape, complete capsule, and uniform internal echo, cystic solid nodules with mainly cystic or cystic nodules without comet tail sign are considered as benign; A solid nodule with nonuniform internal echo and accompanied by one of the following features is diagnosed as malignant: unclear boundary and irregular shape; Internal microcalcification; Aspect ratio >1, while TI‐RADS 4 nodules can show other features except cystic 11–13 . With the advent of a large number of new methods for diagnosing thyroid nodules, it is increasingly rare to use conventional ultrasound alone to diagnose thyroid nodules 14,15 …”

Section: Conventional Ultrasoundmentioning

confidence: 99%

Multimodal ultrasound imaging: A method to improve the accuracy of diagnosing thyroid TI‐RADS 4 nodules

Han

Huang

Wang

et al. 2022

J of Clinical Ultrasound

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Thyroid nodule is a common and frequently occurring disease in the neck in recent years, and ultrasound has become the preferred imaging diagnosis method for thyroid nodule due to its advantages of noninvasive, nonradiation, real‐time, and repeatable. The thyroid imaging, reporting and data system (TI‐RADS) classification standard scores suspicious nodules that are difficult to determine benign and malignant as grade 4, and further pathological puncture is recommended clinically, which may lead to a large number of unnecessary biopsies and operations. Including conventional ultrasound, ACR TI‐RADS, shear wave elastography, super microvascular imaging, contrast enhanced ultrasound, “firefly,” artificial intelligence, and multimodal ultrasound imaging used in combination. In order to identify the most clinically significant malignant tumors when reducing invasive operations. This article reviews the application and research progress of multimodal ultrasound imaging in the diagnosis of TI‐RADS 4 thyroid nodules.

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Prediction of suspicious thyroid nodule using artificial neural network based on radiofrequency ultrasound and conventional ultrasound: A preliminary study

Cited by 20 publications

References 22 publications

A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules

A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules

Systematic review and meta-analysis: diagnostic value of different ultrasound for benign and malignant thyroid nodules

Multimodal ultrasound imaging: A method to improve the accuracy of diagnosing thyroid TI‐RADS 4 nodules

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