False-Positive Malignant Diagnosis of Nodule Mimicking Lesions by Computer-Aided Thyroid Nodule Analysis in Clinical Ultrasonography Practice

Artificial intelligence in thyroid ultrasound

Artificial intelligence (AI), particularly deep learning (DL) algorithms, has demonstrated remarkable progress in image-recognition tasks, enabling the automatic quantitative assessment of complex medical images with increased accuracy and efficiency. AI is widely used and is becoming increasingly popular in the field of ultrasound. The rising incidence of thyroid cancer and the workload of physicians have driven the need to utilize AI to efficiently process thyroid ultrasound images. Therefore, leveraging AI in thyroid cancer ultrasound screening and diagnosis cannot only help radiologists achieve more accurate and efficient imaging diagnosis but also reduce their workload. In this paper, we aim to present a comprehensive overview of the technical knowledge of AI with a focus on traditional machine learning (ML) algorithms and DL algorithms. We will also discuss their clinical applications in the ultrasound imaging of thyroid diseases, particularly in differentiating between benign and malignant nodules and predicting cervical lymph node metastasis in thyroid cancer. Finally, we will conclude that AI technology holds great promise for improving the accuracy of thyroid disease ultrasound diagnosis and discuss the potential prospects of AI in this field.

Ultrasonic S-Detect mode for the evaluation of thyroid nodules: A meta-analysis

OBJECTIVES

This meta-analysis aimed to evaluate the value of ultrasonic S-Detect mode for the evaluation of thyroid nodules.

METHODS

We searched PubMed, Cochrane Library, and Chinese biomedical databases from inception to August 31, 2021. Meta-analysis was conducted using STATA version 14.0 and Meta-Disc version 1.4 software. We calculated the summary statistics for sensitivity (Sen), specificity (Spe), summary receiver operating characteristic curve, and the area under the curve, and compared the area under the curve between ultrasonic S-Detect mode and thyroid imaging report and data system (TI-RADS) for the diagnosis of thyroid nodules. As a systematic review summarizing the results of previous studies, this study does not need the informed consent of patients or the approval of the ethics review committee.

RESULTS

Fifteen studies that met all inclusion criteria were included in this meta-analysis. A total of 924 thyroid malignant nodules and 1228 thyroid benign nodules were assessed. All thyroid nodules were histologically confirmed after examination. The pooled Sen and Spe of TI-RADS were 0.89 (95% confidence interval [CI] = 0.85-0.91) and 0.85 (95% CI = 0.78-0.90), respectively; the pooled Sen and Spe of S-Detect were 0.88 (95% CI = 0.85-0.90) and 0.73 (95% CI = 0.63-0.81), respectively. The areas under the summary receiver operating characteristic curve of TI-RADS and S-Detect were 0.9370 (standard error [SE] = 0.0110) and 0.9128 (SE = 0.0147), respectively, between which there was no significant difference (Z = 1.318; SE = 0.0184; P = .1875). We found no evidence of publication bias (t = 0.36, P = .72).

CONCLUSIONS

Our meta-analysis indicates that ultrasonic S-Detect mode may have high diagnostic accuracy and may have certain clinical application value, especially for young doctors.

Diagnostic accuracy of S-Detect in distinguishing benign and malignant thyroid nodules: A meta-analysis

Objectives

In this meta-analysis study, the main objective was to determine the accuracy of S-detect in effectively distinguishing malignant thyroid nodules from benign thyroid nodules.

Methods

We searched the PubMed, Cochrane Library, and CBM databases from inception to August 1, 2021. Meta-analysis was conducted using STATA version 14.0 and Meta-Disc version 1.4 softwares. We calculated summary statistics for sensitivity (Sen), specificity (Spe), positive and negative likelihood ratio (LR⁺/LR⁻), diagnostic odds ratio(DOR), and receiver operating characteristic (SROC) curves. Cochran’s Q-statistic and I² test were used to evaluate potential heterogeneity between studies. A sensitivity analysis was performed to evaluate the influence of single studies on the overall estimate. We also performed meta-regression analyses to investigate the potential sources of heterogeneity.

Results

In this study, a total of 17 studies meeting the requirements of the standard were used. The number of benign and malignant nodules analyzed and evaluated in this paper was 1595 and 1118 respectively. This paper mainly completes the required histological confirmation through s-detect. The pooled Sen and pooled Spe were 0.87 and 0.74, respectively, (95%CI = 0.84–0.89) and (95%CI = 0.66–0.81). Furthermore, the pooled LR⁺ and negative LR⁻ were determined to be 3.37 (95%CI = 2.53–4.50) and 0.18 (95%CI = 0.15–0.21), respectively. The experimental results showed that the pooled DOR of thyroid nodules was 18.83 (95% CI = 13.21–26.84). In addition, area under SROC curve was determined to be 0.89 (SE = 0.0124). It should be pointed out that there is no evidence of bias (i.e. t = 0.25, P = 0.80).

Conclusions

Through this meta-analysis, it can be seen that the accuracy of s-detect is relatively high for the effective distinction between malignant thyroid nodules and benign thyroid nodules.

A comparison of artificial intelligence versus radiologists in the diagnosis of thyroid nodules using ultrasonography: a systematic review and meta-analysis

BACKGROUND

Thyroid nodules are common. Ultrasonography (US) is the first investigation for thyroid nodules. Artificial Intelligence (AI) is widely integrated into medical diagnosis to provide additional information. The primary objective of this study was to accumulate the pooled sensitivity and specificity between all available AI and radiologists using thyroid US imaging. The secondary objective was to compare AI's diagnostic performance to that of radiologists.

MATERIALS AND METHODS

A systematic review meta-analysis. PubMed, Scopus, Web of Science, and Cochrane Library data were searched for studies from inception until June 11, 2020.

RESULTS

Twenty five studies were included in this meta-analysis. The pooled sensitivity and specificity of AI were 0.86 (95% CI 0.81-0.91) and 0.78 (95% CI 0.73-0.83), respectively. The pooled sensitivity and specificity of radiologists were 0.85 (95% CI 0.80-0.89) and 0.82 (95% CI 0.77-0.86), respectively. The accuracy of AI and radiologists is equivalent in terms of AUC [AI 0.89 (95% CI 0.86-0.92), radiologist 0.91 (95% CI 0.88-0.93)]. The diagnostic odd ratio (DOR) between AI 23.10 (95% CI 14.20-37.58) and radiologists 27.12 (95% CI 17.45-42.16) had no statistically significant difference (P = 0.56). Meta-regression analysis revealed that Deep Learning AI had significantly greater sensitivity and specificity than classic machine learning AI (P < 0.001).

CONCLUSION

AI demonstrated comparable performance to radiologists in diagnosing benign and malignant thyroid nodules using ultrasonography. Additional research to establish its equivalency should be conducted.