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Artificial intelligence-aided method to detect uterine fibroids in ultrasound images: a retrospective study
Artificial intelligence-aided method to detect uterine fibroids in ultrasound images: a retrospective study
REVIEWED BY Robyn Boman, AFASA | ASA SIG: Emerging Technologies
REFERENCE | Authors: Tongtong Huo, Lixin Li, Xiting Chen, Ziyi Wang, Xiaojun Zhang, Songxiang Liu, Jinfa Huang, Jiayao Zhang, Qian Yang, Wei Wu, Yi Xie, Honglin Wang, Zhewei Ye & Kaixian Deng
WHY THE STUDY WAS PERFORMED
This study was performed to assess the effectiveness of an artificial intelligence (AI) program for the detection of uterine fibroids from transabdominal (2–7 MHz) and transvaginal (5–7 MHz) ultrasound images.
The study aimed to analyse if the use of the AI program could assist junior sonographers in detecting uterine fibroids and compare the findings to the detection rate of senior sonographers. The ability of the senior sonographer was the ground truth for the AI program analysis.
HOW THE STUDY WAS PERFORMED
This study was a noninterventional and retrospective study of the efficacy of an artificial program to identify uterine fibroids. The fibroids were all sonographically and pathologically identified. The training of the algorithm used 3870 ultrasound images in total.
There were 2020 images with uterine fibroids (n = 667, mean age = 42.5 years ± 6.23) and 1850 normal images (n = 570, mean age = 39.2 years ± 5.3). A deep convolutional neural network, which is based upon a deep learning algorithm, used 3382 after exclusions to create the AI program. The ground truth was established by sonographers with more than ten years of clinical experience for the confirmation of observed fibroids.
The testing of the efficacy of the AI program was divided into four combinations with each category using averaged results. The sonographers were junior sonographers (n = 4) with 5 or fewer years of experience and senior sonographers (n = 4) with 10 or more years of experience. The two levels of experience were compared against the AI program.
Additionally, the junior sonographers and AI program combined were compared against the senior sonographers.
The proposed DCNN detection system can identify the presence of fibroids in ultrasound images, and it can also serve as a learning tool for junior sonographers to learn to correctly differentiate uterine fibroids.
WHAT THE STUDY FOUND
The study demonstrated there was an improvement in the detection of uterine fibroids using the AI program compared to the detection rate of junior sonographers. There was also an improvement in the detection of uterine fibroids for AI-assisting junior sonographers compared to junior sonographers with no assistance from the AI program.
Incidentally, there was no statistical difference between the findings of the junior sonographers. Additionally, there was no statistical difference between the senior sonographers identifying uterine fibroids with or without the assistance of the AI program.
With the use of the AI program, the junior sonographers demonstrated the same efficacy as the senior sonographers without AI. The area under the curve (AUC) defines the characteristic performance of the testing. Junior sonographers demonstrated an AUC of 0.87 while the senior sonographers, AI program alone and AI program combined with junior sonographers, were all 0.95. These results demonstrate the effectiveness of the AI program tested as a teaching tool.
RELEVANCE TO CLINICAL PRACTICE
The development of this AI program has been demonstrated to be an effective tool in assisting junior sonographers to improve their ability to detect uterine fibroids. The AI program has the potential to improve detection rates while assisting junior sonographers in the process of identifying uterine fibroids (Table 1).
PPV positive predictive value, NPV negative predictive value, AUC area under the receiver operating characteristic curve. P*1 value for junior ultrasonographers (averaged) compared to DCNN. P*2 value for senior ultrasonographers (averaged) compared to DCNN. P*3 value for DCNN + junior ultrasonographers (averaged) compared to junior ultrasonographers (averaged). P*4 value for DCNN + junior ultrasonographers (averaged) compared to senior ultrasonographers (averaged).
