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One step further into the blackbox: a pilot study of how to build more confidence around an AI-based decision system of breast nodule assessment in 2D ultrasound

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To investigate how a DL model makes decisions in lesion classification with a newly defined region of evidence (ROE) by incorporating “explainable AI” (xAI) techniques.

Methods

A data set of 785 2D breast ultrasound images acquired from 367 females. The DenseNet-121 was used to classify whether the lesion is benign or malignant. For performance assessment, classification results are evaluated by calculating accuracy, sensitivity, specificity, and receiver operating characteristic for experiments of both coarse and fine regions of interest (ROIs). The area under the curve (AUC) was evaluated, and the true-positive, false-positive, true-negative, and false-negative results with breakdown in high, medium, and low resemblance on test sets were also reported.

Results

The two models with coarse and fine ROIs of ultrasound images as input achieve an AUC of 0.899 and 0.869, respectively. The accuracy, sensitivity, and specificity of the model with coarse ROIs are 88.4%, 87.9%, and 89.2%, and with fine ROIs are 86.1%, 87.9%, and 83.8%, respectively. The DL model captures ROE with high resemblance of physicians’ consideration as they assess the image.

Conclusions

We have demonstrated the effectiveness of using DenseNet to classify breast lesions with limited quantity of 2D grayscale ultrasound image data. We have also proposed a new ROE-based metric system that can help physicians and patients better understand how AI makes decisions in reading images, which can potentially be integrated as a part of evidence in early screening or triaging of patients undergoing breast ultrasound examinations.

Key Points

• The two models with coarse and fine ROIs of ultrasound images as input achieve an AUC of 0.899 and 0.869, respectively. The accuracy, sensitivity, and specificity of the model with coarse ROIs are 88.4%, 87.9%, and 89.2%, and with fine ROIs are 86.1%, 87.9%, and 83.8%, respectively.

• The first model with coarse ROIs is slightly better than the second model with fine ROIs according to these evaluation metrics.

• The results from coarse ROI and fine ROI are consistent and the peripheral tissue is also an impact factor in breast lesion classification.

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Abbreviations

AI:

Artificial intelligence

AUC:

Area under the curve

CNN:

Convolutional neural network

DL:

Deep learning

FN:

False negative

FP:

False positive

Grad-CAM:

Gradient-weighted class activation mapping

HC:

High confidence

HR:

High resemblance

LC:

Low confidence

LR:

Low resemblance

MC:

Medium confidence

MR:

Medium resemblance

RCTs:

Randomized controlled trials

RNN:

Recurrent neural network

ROC:

Receiver operating characteristic

ROE:

Region of evidence

ROI:

Region of interest

TN:

True negative

TP:

True positive

US:

Ultrasound

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Acknowledgements

This project was supported by the Medical Science and Technology Research Foundation of Guangdong (B2019045, project approval, but non-subsidy).

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. The Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education and Chinese Ministry of Health, and The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Qilu Hospital of Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, People’s Republic of China

    Fajin Dong, Chen Cui, Siyuan Shi & Yun Zhang

  2. Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, 518020, People’s Republic of China

    Fajin Dong, Xuqiao Hu, Jieying Zeng, Huaiyu Wu & Jinfeng Xu

  3. Department of Obstetrics and Gynecology, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, 518020, People’s Republic of China

    Ruilian She

Authors
  1. Fajin Dong
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  2. Ruilian She
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  3. Chen Cui
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  4. Siyuan Shi
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  8. Jinfeng Xu
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  9. Yun Zhang
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Corresponding authors

Correspondence to Jinfeng Xu or Yun Zhang.

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Guarantor

The scientific guarantor of this publication is Jinfeng Xu, who is the director of Ultrasound Department of Shenzhen People’s Hospital.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

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Written informed consent was obtained from all subjects (patients) in this study.

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Institutional Review Board approval was obtained.

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• retrospective

• diagnostic or prognostic study

• performed at one institution

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Dong, F., She, R., Cui, C. et al. One step further into the blackbox: a pilot study of how to build more confidence around an AI-based decision system of breast nodule assessment in 2D ultrasound. Eur Radiol 31, 4991–5000 (2021). https://doi.org/10.1007/s00330-020-07561-7

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  • DOI: https://doi.org/10.1007/s00330-020-07561-7

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