Literature DB >> 29286180

Computer-aided system for diagnosing thyroid nodules on ultrasound: A comparison with radiologist-based clinical assessments.

Luying Gao1, Ruyu Liu1, Yuxin Jiang1, Wenfeng Song2, Ying Wang1, Jia Liu1, Juanjuan Wang1, Dongqian Wu1, Shuai Li2, Aimin Hao2, Bo Zhang1.   

Abstract

BACKGROUND: The purpose of this study was to compare the diagnostic efficiency of a thyroid ultrasound computer-aided diagnosis (CAD) system with that of 1 radiologist.
METHODS: This study retrospectively reviewed 342 surgically resected thyroid nodules from July 2013 to December 2013 at our center. The nodules were assessed on typical ultrasound images using the CAD system and reviewed by 1 experienced radiologist. The radiologist stratified the risk of malignancy using the Thyroid Imaging Reporting and Data Systems (TIRADS) and the American Thyroid Association (ATA) guidelines.
RESULTS: The radiologist, using TI-RADS and ATA guidelines, performed better than the CAD system (P < .01). The sensitivity of the CAD system was similar to that of an experienced radiologist (P > .05; P < .01; and P > .05). However, we found that the CAD system had lower specificity (P < .01).
CONCLUSION: The sensitivity of a thyroid ultrasound CAD system in differentiating nodules was similar to that of an experienced radiologist. However, the CAD system had lower specificity.
© 2017 Wiley Periodicals, Inc.

Entities:  

Keywords:  2015 American Thyroid Association management guideline; Thyroid Imaging Reporting and Data System; artificial intelligence; computer-aided diagnosis system; thyroid nodules; ultrasound

Mesh:

Year:  2017        PMID: 29286180     DOI: 10.1002/hed.25049

Source DB:  PubMed          Journal:  Head Neck        ISSN: 1043-3074            Impact factor:   3.147


  20 in total

1.  Computer-aided diagnostic system for thyroid nodule sonographic evaluation outperforms the specificity of less experienced examiners.

Authors:  Daniele Fresilli; Giorgio Grani; Maria Luna De Pascali; Gregorio Alagna; Eleonora Tassone; Valeria Ramundo; Valeria Ascoli; Daniela Bosco; Marco Biffoni; Marco Bononi; Vito D'Andrea; Fabrizio Frattaroli; Laura Giacomelli; Yana Solskaya; Giorgia Polti; Patrizia Pacini; Olga Guiban; Raffaele Gallo Curcio; Marcello Caratozzolo; Vito Cantisani
Journal:  J Ultrasound       Date:  2020-04-03

2.  Interobserver agreement and efficacy of consensus reading in Kwak-, EU-, and ACR-thyroid imaging recording and data systems and ATA guidelines for the ultrasound risk stratification of thyroid nodules.

Authors:  Philipp Seifert; Rainer Görges; Michael Zimny; Michael C Kreissl; Simone Schenke
Journal:  Endocrine       Date:  2019-11-18       Impact factor: 3.633

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

Authors:  Pimrada Potipimpanon; Natamon Charakorn; Prakobkiat Hirunwiwatkul
Journal:  Eur Arch Otorhinolaryngol       Date:  2022-06-29       Impact factor: 3.236

Review 4.  Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis.

Authors:  Lei Xu; Junling Gao; Quan Wang; Jichao Yin; Pengfei Yu; Bin Bai; Ruixia Pei; Dingzhang Chen; Guochun Yang; Shiqi Wang; Mingxi Wan
Journal:  Eur Thyroid J       Date:  2019-12-04

5.  An efficient deep convolutional neural network model for visual localization and automatic diagnosis of thyroid nodules on ultrasound images.

Authors:  Jialin Zhu; Sheng Zhang; Ruiguo Yu; Zhiqiang Liu; Hongyan Gao; Bing Yue; Xun Liu; Xiangqian Zheng; Ming Gao; Xi Wei
Journal:  Quant Imaging Med Surg       Date:  2021-04

6.  Deep Learning Based on ACR TI-RADS Can Improve the Differential Diagnosis of Thyroid Nodules.

Authors:  Ge-Ge Wu; Wen-Zhi Lv; Rui Yin; Jian-Wei Xu; Yu-Jing Yan; Rui-Xue Chen; Jia-Yu Wang; Bo Zhang; Xin-Wu Cui; Christoph F Dietrich
Journal:  Front Oncol       Date:  2021-04-27       Impact factor: 6.244

7.  Effectiveness evaluation of computer-aided diagnosis system for the diagnosis of thyroid nodules on ultrasound: A systematic review and meta-analysis.

Authors:  Wan-Jun Zhao; Lin-Ru Fu; Zhi-Mian Huang; Jing-Qiang Zhu; Bu-Yun Ma
Journal:  Medicine (Baltimore)       Date:  2019-08       Impact factor: 1.817

8.  Clinical validation of S-DetectTM mode in semi-automated ultrasound classification of thyroid lesions in surgical office.

Authors:  Marcin Barczyński; Małgorzata Stopa-Barczyńska; Beata Wojtczak; Agnieszka Czarniecka; Aleksander Konturek
Journal:  Gland Surg       Date:  2020-02

9.  Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques.

Authors:  Vijay Vyas Vadhiraj; Andrew Simpkin; James O'Connell; Naykky Singh Ospina; Spyridoula Maraka; Derek T O'Keeffe
Journal:  Medicina (Kaunas)       Date:  2021-05-24       Impact factor: 2.430

10.  Deep Learning for Differentiating Benign From Malignant Parotid Lesions on MR Images.

Authors:  Xianwu Xia; Bin Feng; Jiazhou Wang; Qianjin Hua; Yide Yang; Liang Sheng; Yonghua Mou; Weigang Hu
Journal:  Front Oncol       Date:  2021-06-23       Impact factor: 6.244

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