.png){kind=icon}
返回
Combining Image Similarity and Predictive Artificial Intelligence Models to Decrease Subjectivity in Thyroid Nodule Diagnosis and Improve Malignancy Prediction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eprac.2024.08.001.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Objectives: To evaluate the efficacy of combining predictive artificial intelligence (AI) and image similarity model to risk stratify thyroid nodules, using retrospective external validation study. Methods: Two datasets were used to determine efficacy of the AI application. One was Stanford dataset ultrasound images of 192 nodules between April 2017 and May 2018 and the second was private practice consisting of 118 thyroid nodule images between January 2018 and December 2023. The nodules had definitive diagnosis by cytology or surgical pathology. The AI application was used to predict the diagnosis and American College of Radiology Thyroid Imaging and Data System (ACR TI-RADS) score. Results: In the Stanford dataset, the AI application predicted malignancies with sensitivity of 1.0 and specificity of 0.55. Positive predictive value (PPV) was 0.18 and negative predictive value (NPV) was 1.0. The Area Under the Curve- Receiver Operating Characteristic was 0.78. ACR TI-RADS based clinical recommendation had a polychoric correlation of 0.67. In the private dataset, the AI application predicted malignancies with sensitivity of 0.91 and specificity of 0.95. PPV was 0.8 and NPV was 0.98. The area under the curve- receiver operating characteristic was 0.93 and accuracy was 0.94. ACR TI-RADS based score had a polychoric correlation of 0.94. Conclusion: The AI application showed good performance for sensitivity and NPV between the two datasets and demonstrated potential for 61.5% reduction in the need for fine needle aspiration and strong correlation to ACR TI-RADS. However, PPV was variable between the datasets possibly from variability in image selection and prevalence of malignancy. If implemented widely and consistently among various clinical settings, this could lead to decreased patient burden associated with an invasive procedure and possibly to decreased health care spending.
Keyword:
thyroid nodule
AI
TI-RADS
minimally invasive
thyroid cancer
thyroid malignancy
image similarity
predictive AI
期刊
IF:
4.6
论文数:
3.3K
被引数:
6.4K
机构
引用论文
分享收藏Cost Impact of Molecular Testing for Indeterminate Thyroid Nodule Fine-Needle Aspiration Biopsies
OAAI
OAAI分享收藏分享收藏分享收藏High-performance medicine: the convergence of human and artificial intelligence高性能医学: 人类与人工智能的融合.png)
NATURE MEDICINE
NATURE MEDICINEIF50
PREAI
分享收藏A framework for validating AI in precision medicine: considerations from the European ITFoC consortium在精准医学中验证AI的框架: 来自欧洲ITFoC联盟的考虑
OAAI
OAAI分享收藏External validation of AIBx, an artificial intelligence model for risk stratification, in thyroid nodules甲状腺结节中风险分层的人工智能模型AIBx的外部验证OAAI
OAAI分享收藏
查看更多内容