Improved Diagnostic Accuracy of Thyroid Fine-Needle Aspiration Cytology with Artificial Intelligence Technology-Reference-Cited by-同舟云学术

Improved Diagnostic Accuracy of Thyroid Fine-Needle Aspiration Cytology with Artificial Intelligence Technology

Published:2024-06-01 Issue:6 Volume:34 Page:723-734
ISSN:1050-7256
Container-title:Thyroid®
language:en
Short-container-title:Thyroid®

Author:

Lee Yujin¹,Alam Mohammad Rizwan²,Park Hongsik¹,Yim Kwangil²,Seo Kyung Jin²^ORCID,Hwang Gisu³,Kim Dahyeon³,Chung Yeonsoo³,Gong Gyungyub⁴,Cho Nam Hoon⁵,Yoo Chong Woo⁶,Chong Yosep²^ORCID,Choi Hyun Joo¹

Affiliation:

1. Department of Hospital Pathology, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea.

2. Department of Hospital Pathology, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea.

3. AI Team, DeepNoid Inc., Seoul, Korea.

4. Department of Pathology, Asan Medical Center, Seoul, Korea.

5. Department of Pathology, Yonsei University College of Medicine, Seoul, Korea.

6. Department of Pathology, National Cancer Center, Ilsan, Republic of Korea.

Publisher

Mary Ann Liebert Inc

Link

https://www.liebertpub.com/doi/pdf/10.1089/thy.2023.0384

Reference43 articles.

1. Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future

2. Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

3. Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape

4. Development and Validation of a Machine Learning Model for Detection and Classification of Tertiary Lymphoid Structures in Gastrointestinal Cancers

5. Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid