Scholarlink

Purpose <p>This study explores a self-learning method as an auxiliary approach in residency training for distinguishing between benign and malignant thyroid nodules.</p> Methods <p>Conducted from March to December 2022, internal medicine residents underwent three repeated learning sessions with a “learning set” comprising 3000 thyroid nodule images. Diagnostic performances for internal medicine residents were assessed before the study, after every learning session, and for radiology residents before and after one-on-one education, using a “test set,” comprising 120 thyroid nodule images. Finally, all residents repeated the same test using artificial intelligence computer-assisted diagnosis (AI-CAD).</p> Results <p>Twenty-one internal medicine and eight radiology residents participated. Initially, internal medicine residents had a lower area under the receiver operating characteristic curve (AUROC) than radiology residents (0.578 vs. 0.701, <i>P</i> &lt; 0.001), improving post-learning (0.578 to 0.709, <i>P</i> &lt; 0.001) to a comparable level with radiology residents (0.709 vs. 0.735, <i>P</i> = 0.17). Further improvement occurred with AI-CAD for both group (0.709 to 0.755, <i>P</i> &lt; 0.001; 0.735 to 0.768, <i>P</i> = 0.03).</p> Conclusion <p>The proposed iterative self-learning method using a large volume of ultrasonographic images can assist beginners, such as residents, in thyroid imaging to differentiate benign and malignant thyroid nodules. Additionally, AI-CAD can improve the diagnostic performance across varied levels of experience in thyroid imaging.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing diagnostic accuracy of thyroid nodules: integrating self-learning and artificial intelligence in clinical training

  • 期刊论文

  • 发表日期：

    2025年2月20日

查看全文
  • Daham Kim,
  • Yoon-a Hwang,
  • Youngsook Kim,
  • Hye Sun Lee,
  • Eunjung Lee,
  • Hyunju Lee,
  • Jung Hyun Yoon,
  • Vivian Youngjean Park,
  • Miribi Rho,
  • Jiyoung Yoon,
  • Si Eun Lee,
  • Jin Young Kwak

摘要

Purpose

This study explores a self-learning method as an auxiliary approach in residency training for distinguishing between benign and malignant thyroid nodules.

Methods

Conducted from March to December 2022, internal medicine residents underwent three repeated learning sessions with a “learning set” comprising 3000 thyroid nodule images. Diagnostic performances for internal medicine residents were assessed before the study, after every learning session, and for radiology residents before and after one-on-one education, using a “test set,” comprising 120 thyroid nodule images. Finally, all residents repeated the same test using artificial intelligence computer-assisted diagnosis (AI-CAD).

Results

Twenty-one internal medicine and eight radiology residents participated. Initially, internal medicine residents had a lower area under the receiver operating characteristic curve (AUROC) than radiology residents (0.578 vs. 0.701, P < 0.001), improving post-learning (0.578 to 0.709, P < 0.001) to a comparable level with radiology residents (0.709 vs. 0.735, P = 0.17). Further improvement occurred with AI-CAD for both group (0.709 to 0.755, P < 0.001; 0.735 to 0.768, P = 0.03).

Conclusion

The proposed iterative self-learning method using a large volume of ultrasonographic images can assist beginners, such as residents, in thyroid imaging to differentiate benign and malignant thyroid nodules. Additionally, AI-CAD can improve the diagnostic performance across varied levels of experience in thyroid imaging.