基于深度学习的肝外伤超声图像自动识别模型

doi:10.3877/cma.j.issn.1672-6448.2022.03.002

中华医学超声杂志（电子版） ›› 2022, Vol. 19 ›› Issue (03) : 195 -199. doi: 10.3877/cma.j.issn.1672-6448.2022.03.002

腹部超声影像学

基于深度学习的肝外伤超声图像自动识别模型

王妍洁¹, 罗渝昆²^,^†(

), 何雪磊³, 宋青², 王坤⁴, 马骏¹, 韩鹏², 李朔朔², 康林立²

^1. 100853　北京，解放军总医院第一医学中心超声诊断科；100853　北京，解放军医学院
^2. 100853　北京，解放军总医院第一医学中心超声诊断科
^3. 100190　北京，中科院自动化研究所分子影像重点实验室；710127　西安，西北大学信息科学与技术学院
^4. 100190　北京，中科院自动化研究所分子影像重点实验室

收稿日期:2021-12-08 出版日期:2022-03-01
通信作者: 罗渝昆
基金资助:
解放军总医院临床科研扶持基金(ZH19021); 中国博士后基金面上项目(2018M643876); 国家自然科学基金(81971635)

An automatic deep learning-based recognition model for liver trauma ultrasound images

Yanjie Wang¹, Yukun Luo²^,^†(), Xuelei He³, Qing Song², Kun Wang⁴, Jun Ma¹, Peng Han², Shuoshuo Li², Linli Kang²

^1. Department of Ultrasound, the First Medical Center of Chinese PLA General Hospital, Beijing 100853, China; Chinese PLA Medical School, Beijing 100853, China
^2. Department of Ultrasound, the First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
^3. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Information Sciences and Technology, Northwest University, Xi'an 710127, China
^4. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Received:2021-12-08 Published:2022-03-01
Corresponding author: Yukun Luo

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引用本文：

王妍洁, 罗渝昆, 何雪磊, 宋青, 王坤, 马骏, 韩鹏, 李朔朔, 康林立. 基于深度学习的肝外伤超声图像自动识别模型[J/OL]. 中华医学超声杂志（电子版）, 2022, 19(03): 195-199.

Yanjie Wang, Yukun Luo, Xuelei He, Qing Song, Kun Wang, Jun Ma, Peng Han, Shuoshuo Li, Linli Kang. An automatic deep learning-based recognition model for liver trauma ultrasound images[J/OL]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2022, 19(03): 195-199.

目的

建立基于深度学习的卷积神经网络肝损伤模型（CNLDM），并评估其对肝实质挫裂伤的诊断价值。

方法

通过动物实验获得2009张含有肝实质挫裂伤超声图像及1302张正常肝超声图像，作为模型的训练集和验证集。回顾性收集2015年1月至2021年4月解放军总医院第一医学中心确诊存在肝实质挫裂伤的超声图像153张，以及81张不含肝实质挫裂伤的肝超声图像，作为模型的外部测试集。6名不同年资医师分别对测试集图像数据进行判读。使用受试者操作特征（ROC）曲线及决策曲线分析（DCA）检验模型效能，比较不同年资医师与CNLDM模型预测肝实质挫裂伤的敏感度、特异度、准确性、阴性预测值及阳性预测值。

结果

CNLDM模型诊断效能（敏感度为80%，特异度为77%，阳性预测值为87%，阴性预测值为66%）优于低年资医师组（敏感度为61%，特异度为75%，阳性预测值为82%，阴性预测值为51%），略差于高年资医师组（敏感度为84%，特异度为86%，阳性预测值为92%，阴性预测值为75%），差异具有统计学意义（H=15.306，P＜0.001；H=3.289，P＜0.001），而模型效能与中年资医师组接近，差异无统计学意义（P＞0.05）。DCA显示模型在阈值0.4~0.6之间有较好的测试集收益。

结论

基于超声的人工智能模型可以较为准确地区分正常肝与含有肝实质挫裂伤的异常肝，对进一步指导临床诊治工作具有重要的意义。

关键词: 人工智能, 深度学习, 肝外伤, 诊断, 超声

Objective

To establish a convolutional neural network-based liver injury diagnostic model (CNLDM) and evaluate its diagnostic value for liver trauma.

Methods

A total of 2009 ultrasound images of liver trauma and 1302 ultrasound images of normal liver were obtained through animal experiments, which were used as the training set and validation set of the model. As an external test set of the model, a retrospective collection of 153 ultrasound images of liver trauma and 81 liver ultrasound images without liver trauma was performed at the First Medical Center of Chinese PLA General Hospital from January 2015 to April 2021. Six doctors of different seniority interpreted the external test set, respectively. Receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA) were used to test the performance of the model, and the differences between the six physicians and CNLDM model in sensitivity, specificity, accuracy, negative predictive value, and positive predictive value of liver trauma were compared.

Results

The diagnostic performance of the CNLDM (sensitivity, 80%; specificity, 77%; positive predictive value, 87%; negative predictive value, 66%) was better than that of the junior physician group (sensitivity, 61%; specificity, 75%; positive predictive value, 82%; negative predictive value, 51%) (H=15.306, P＜0.001), inferior to that of the senior physician group (sensitivity, 84%; specificity, 86%; positive predictive value, 92%; negative predictive value, 75%) (H=3.289, P＜0.001), and close to that of the medium physician group (P＞0.05). DCA showed that the model had good test set returns when the threshold was between 0.4-0.6.

Conclusion

The artificial intelligence-based ultrasound model can accurately distinguish normal liver from abnormal liver with trauma, which is of great significance for further guiding clinical diagnosis and treatment.

Key words: Artificial intelligence, Deep learning, Hepatic trauma, Diagnosis, Ultrasonography

图1 卷积神经网络肝损伤模型构建示意图

图2 卷积神经网络肝损伤模型（CNDLM）数据分配及模型整体框架图

图3 卷积神经网络肝损伤模型（CNLDM）与不同年资医师组在测试集中的效能分析。蓝色曲线为CNLDM模型预测效能受试者操作特征曲线。图中三个点为低、中、高年资医师组诊断的平均效能（

x ˉ

±s）

x ˉ

±s）

图4 卷积神经网络肝损伤模型的决策曲线分析。对于测试集曲线，阈值在0.4~0.6之间，模型有高于0.6的左右的收益

表1 CNLDM模型与不同年资医师识别肝实质挫裂伤的效能对比

组别	敏感度	特异度	阳性预测值	阴性预测值	准确性
CNLDM模型	0.80（0.61~0.91）	0.77（0.65~0.95）	0.87（0.82~0.96）	0.66（0.53~0.80）	0.79（0.71~0.84）
低年资^a	0.61±0.14	0.75±0.03	0.82±0.02	0.51±0.08	0.66±0.08
中年资^b	0.72±0.02	0.89±0.03	0.92±0.02	0.63±0.03	0.78±0.03
高年资^c	0.84±0.08	0.86±0.06	0.92±0.03	0.75±0.09	0.85±0.03

表1 CNLDM模型与不同年资医师识别肝实质挫裂伤的效能对比

组别	敏感度	特异度	阳性预测值	阴性预测值	准确性
CNLDM模型	0.80（0.61~0.91）	0.77（0.65~0.95）	0.87（0.82~0.96）	0.66（0.53~0.80）	0.79（0.71~0.84）
低年资^a	0.61±0.14	0.75±0.03	0.82±0.02	0.51±0.08	0.66±0.08
中年资^b	0.72±0.02	0.89±0.03	0.92±0.02	0.63±0.03	0.78±0.03
高年资^c	0.84±0.08	0.86±0.06	0.92±0.03	0.75±0.09	0.85±0.03

图5 模型案例的输出结果可视化。热图是以模型计算的最后一层特征层，即高维特征与输入图像叠加得到的可视化图像，其中红色部分为模型重点关注区域，即模型经过学习，预测图像中存在肝创伤概率较高的区域

1	van As AB, Millar AJW. Management of paediatric liver trauma [J]. Pediatr Surg Int, 2017, 33(4): 445-453.
2	Buci S, Torba M, Gjata A, et al. The rate of success of the conservative management of liver trauma in a developing country [J]. World J Emerg Surg, 2017, 12: 24.
3	Padalino P, Bomben F, Chiara O, et al. Healing of blunt liver injury after non-operative management: role of ultrasonography follow-up [J]. Eur J Trauma Emerg Surg, 2009, 35(4): 364-370.
4	Badger SA, Barclay R, Campbell P, et al. Management of liver trauma [J]. World J Surg, 2009, 33(12): 2522-2537.
5	Stengel D, Bauwens K, Rademacher G, et al. Emergency ultrasound-based algorithms for diagnosing blunt abdominal trauma [J]. Cochrane Database Syst Rev, 2015, 2015(9): CD004446.
6	Chan HP, Samala RK, Hadjiiski LM, et al. Deep learning in medical image analysis [J]. Adv Exp Med Biol, 2020, 1213: 3-21.
7	Pehrson LM, Lauridsen C, Nielsen MB. Machine learning and deep learning applied in ultrasound [J]. Ultraschall Med, ,2018, 39(4): 379-381.
8	Brattain LJ, Telfer BA, Dhyani M, et al. Machine learning for medical ultrasound: status, methods, and future opportunities [J]. Abdom Radiol (NY), 2018, 43(4): 786-799.
9	Lai S, Xu L, Liu K, et al. Recurrent convolutional neural networks for text classification [C]. 29th AAAI Conference on Artificial Intelligence, Austin, Texas, USA, 2015.
10	Xie H N, Wang N, He M, et al. Using deep‐learning algorithms to classify fetal brain ultrasound images as normal or abnormal [J]. Ultrasound Obstet Gynecol, 2020, 56(4): 579-587.
11	Xue L, Jiang Z, Fu T, et al. Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis [J]. Eur Radiol, 2020, 30(5): 2973-2983.
12	Zhou R, Fenster A, Xia Y, et al. Deep learning‐based carotid media‐adventitia and lumen‐intima boundary segmentation from three‐dimensional ultrasound images [J]. Med Phys, 2019, 46(7): 3180-3193.
13	Karimi D, Zeng Q, Mathur P, et al. Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images [J]. Med Image Anal, 2019, 57: 186-196.
14	Jin Z, Zhu Y, Zhang S, et al. Ultrasound computer-aided diagnosis (CAD) based on the thyroid imaging reporting and data system (TI-RADS) to distinguish benign from malignant thyroid nodules and the diagnostic performance of radiologists with different diagnostic experience [J]. Med Sci Monit, 2020, 26: e918452.
15	Shia W, Lin L, Chen D. Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches [J]. Sci Rep, 2021, 11(1): 1-11.
16	Kuo C, Chang C, Liu K, et al. Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning [J]. NPJ Digit Med, 2019, 2: 29.
17	Liu F, Liu D, Wang K, et al. Deep learning radiomics based on contrast-enhanced ultrasound might optimize curative treatments for very-early or early-stage hepatocellular carcinoma patients [J]. Liver Cancer, 2020, 9(4): 397-413.
18	Liu D, Liu F, Xie X, et al. Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound [J]. Eur Radiol, 2020, 30(4): 2365-2376.
19	Gondek S, Schroeder ME, Sarani B. Assessment and resuscitation in trauma management [J]. Surg Clin North Am, 2017, 97(5): 985-998.
20	Schembari E, Sofia M, Latteri S, et al. Blunt liver trauma: effectiveness and evolution of non-operative management (NOM) in 145 consecutive cases [J]. Updates Surg, 2020, 72(4): 1065-1071.
21	Parra-Romero G, Contreras-Cantero G, Orozco-Guibaldo D, et al. Trauma abdominal: experiencia de 4961 casos en el occidente de México [J]. Cir Cir, 2019, 87(2): 183-189.
22	战术战伤救治中的超声技术应用专家共识 [J/CD]. 中华医学超声杂志(电子版), 2019, 16(12): 892-898.
23	Recht MP, Dewey M, Dreyer K, et al. Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations [J]. Eur Radiol, 2020, 30(6): 3576-3584.

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