Literature DB >> 31262524

Real-World Performance of Computer-Aided Diagnosis System for Thyroid Nodules Using Ultrasonography.

Hye Lin Kim1, Eun Ju Ha2, Miran Han1.   

Abstract

This study evaluated the diagnostic performance of a commercially available computer-aided diagnosis (CAD) system (S-Detect 1 and S-Detect 2 for thyroid) for detecting thyroid cancers. Among 218 thyroid nodules in 106 patients, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the CAD systems were 80.2%, 82.6%, 75.0%, 86.3% and 81.7%, respectively, for the S-Detect 1 and 81.4%, 68.2%, 62.5%, 84.9% and 73.4%, respectively, for the S-Detect 2. The inter-observer agreement between the CAD system and radiologist for the description of calcifications was fair (kappa = 0.336), while the final diagnosis and each ultrasonographic descriptor showed moderate to substantial agreement for the S-Detect 2. To conclude, the current CAD systems had limited specificity in the diagnosis of thyroid cancer. One of the main limitations of the S-Detect 2 was its inaccuracy in recognizing calcifications, which meant that differentiation had to be undertaken by the radiologist.
Copyright © 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Computer-aided diagnosis; Thyroid cancer; Thyroid nodule; Ultrasonography

Mesh:

Year:  2019        PMID: 31262524     DOI: 10.1016/j.ultrasmedbio.2019.05.032

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  17 in total

1.  Applications of machine learning and deep learning to thyroid imaging: where do we stand?

Authors:  Eun Ju Ha; Jung Hwan Baek
Journal:  Ultrasonography       Date:  2020-07-03

2.  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

3.  [Value of ultrasonic S-Detect technique in diagnosis of breast masses].

Authors:  Y Cheng; Q Xia; J Wang; H Xie; Y Yu; H Liu; Z Yao; J Hu
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2022-07-20

4.  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 5.  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

6.  Convolutional Neural Network to Stratify the Malignancy Risk of Thyroid Nodules: Diagnostic Performance Compared with the American College of Radiology Thyroid Imaging Reporting and Data System Implemented by Experienced Radiologists.

Authors:  G R Kim; E Lee; H R Kim; J H Yoon; V Y Park; J Y Kwak
Journal:  AJNR Am J Neuroradiol       Date:  2021-05-13       Impact factor: 4.966

Review 7.  Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives.

Authors:  Ling-Rui Li; Bo Du; Han-Qing Liu; Chuang Chen
Journal:  Front Oncol       Date:  2021-02-09       Impact factor: 6.244

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.  Computer-Aided Diagnostic System for Thyroid Nodules on Ultrasonography: Diagnostic Performance Based on the Thyroid Imaging Reporting and Data System Classification and Dichotomous Outcomes.

Authors:  M Han; E J Ha; J H Park
Journal:  AJNR Am J Neuroradiol       Date:  2020-12-24       Impact factor: 3.825

10.  Computer-Aided Diagnosis System for the Evaluation of Thyroid Nodules on Ultrasonography: Prospective Non-Inferiority Study according to the Experience Level of Radiologists.

Authors:  Sae Rom Chung; Jung Hwan Baek; Min Kyoung Lee; Yura Ahn; Young Jun Choi; Tae Yon Sung; Dong Eun Song; Tae Yong Kim; Jeong Hyun Lee
Journal:  Korean J Radiol       Date:  2020-03       Impact factor: 3.500

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