Thyroid ultrasonic standard section recognition by artificial  intelligence based on key anatomical structure detection

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Thyroid ultrasonic standard section recognition by artificial intelligence based on key anatomical structure detection

更新时间：2023-10-11

- Thyroid ultrasonic standard section recognition by artificial intelligence based on key anatomical structure detection
- Journal of Clinical Ultrasound in Medicine Vol. 25, Issue 5, (2023)
- 作者机构：
  
  1. 福建医科大学附属第二医院超声医学科
  2. 华侨大学医学院
  3. 泉州医学高等专科学校
  4. 华侨大学工学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2023，
  
  Published Online：26 May 2023，
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Thyroid ultrasonic standard section recognition by artificial intelligence based on key anatomical structure detection. [J]. Journal of Clinical Ultrasound in Medicine 25(5).(2023)
DOI：

Thyroid ultrasonic standard section recognition by artificial intelligence based on key anatomical structure detection. [J]. Journal of Clinical Ultrasound in Medicine 25(5).(2023) DOI：

摘要

目的：探讨基于关键解剖结构检测的人工智能(AI)在甲状腺超声标准切面(TUSP)识别中的价值。方法：以成人TUSP图像为研究对象，含标准集8978张和实验集1916张。标准集分为训练集和验证集，分别用于训练和验证AI模型识别分类TUSP；实验集以超声专家分类为标准，比较分析AI及不同年资医师组对实验集TUSP、非标准切面(non-standard plane，N-SP)图像识别分类能力的差异。结果:AI对8个TUSP切面的分类准确率从94.7%~99.9%不等，对N-SP的分类准确率为93.8%;AI对TUSP及N-SP各切面识别能力均优于初级医生(P<0.05)；AI对左甲状腺纵切面(longitudinal plane of the left lobe of thyroid

LPLT）、N-SP切面的识别能力与中级医师无显著差异（P=0.468 、P=0.816）

对TUSP其余切面识别能力均优于中级医师（P<0.05)。AI效率显著优于各级医师人工分类（P<0.05)。结论:基于关键解剖结构检测的AI对TUSP、N-SP识别分类具有较高准确性和效率，可作为甲状腺超声图像质量控制和专科培训的辅助方法。

Abstract

Objective: To explore the value of artificial intelligence (AI) based on key anatomical structure in thyroid ultrasound standard plane (TUSP) recognition. Methods: Adult thyroid ultrasound imageswere selected as the research objects

including 8978 TUSP images in standard set and 1916 images in experimental set. The images in standard set were further divided into training set and verifying set for training and verifying the ability of AI in recognizing and classifying TUSP. The images in experimental set including TUSP images and non-standard plane (N-SP) images. Taking the classification results of ultrasound experts as the standard

the differences among AI

junior doctors and intermediate doctors in the recognition and classification on experimental set were compared and analyzed.Results: On experimental set

The classification accuracy of AI for eight TUSP sections ranged were from 94.7% to 99.9%

and the classification accuracy of AI for N-SP was 93.8%.AI was superior to junior doctors in the recognition efficiency of TUSP and N-SP (P<0.05).The ability of AI to recognize longitudinal plane of the left lobe of thyroid (LPLT) and N-SP sections was not significantly different from that of intermediate doctors (P=0.468

P=0.816). And AI performed slightly better than intermediate doctors on other sections of TUSP (P<0.05).The classification efficiency of AI was significantly better than the that of artificial classification (P<0.05).Conclusion :AI based on key anatomical structure detection has high accuracy and efficiency for TUSP and N-SP recognition and classification

which can be used as an auxiliary method for thyroid ultrasound image quality control and specialized training.

关键词

人工智能超声检查甲状腺超声标准切面质量控制

Keywords

Artificial intelligenceUltrasonographythyroid ultrasound standard planeQuality control

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