Abstract
Objectives
The Carnegie staging system facilitates the assessment of normal and abnormal development in terms of morphology during the embryonic period. Using virtual reality (VR) it is possible to visually assess the Carnegie stage in-utero, which takes 1-2 minutes per ultrasound image. Adoption in clinical practice is hampered by the need for a VR set-up and required time for visual assessment. To overcome this, our aim is to automate in-utero Carnegie staging using Artificial Intelligence (AI).
Methods
1357 first trimester three-dimensional (3D) ultrasound images of 797 ongoing pregnancies resulting in life birth from The Rotterdam Periconception Cohort were used. We used DenseNet, a state-of-the-art deep learning algorithm for image classification. The algorithm was trained to estimate the Carnegie stage < 16, 16-23, and 23> solely based on the ultrasound images. We used 1100 images of 642 pregnancies for training. For evaluation, we used a test set of 257 images of 155 pregnancies, not used during training.
Results
The AI algorithm achieved an overall accuracy of 61%, which is close to the results of an independent rater, who achieved an accuracy of 63% on 46 images selected for manual VR assessment training. The accuracy was for stage < 16: 55% (n = 9), for stages 16-19: 59% (n = 79), for stages 20-23:62% (n = 151), and for stage >23: 61% (n = 18). The performance differences can partly be explained by the limited size of the embryo early in the first trimester.
Conclusions
Since automatic Carnegie staging using AI is real-time and does not require a VR set-up adoption in clinical practice becomes feasible. In future work, we aim to enhance interpretability by analysing the specific morphological aspects in ultrasound scans utilised by the algorithm to assign the Carnegie stage. Understanding the morphological aspects linked to the Carnegie stage by the algorithm might lead to more in-depth insight into the patterns of normal and abnormal morphological development across pregnancies.
The Carnegie staging system facilitates the assessment of normal and abnormal development in terms of morphology during the embryonic period. Using virtual reality (VR) it is possible to visually assess the Carnegie stage in-utero, which takes 1-2 minutes per ultrasound image. Adoption in clinical practice is hampered by the need for a VR set-up and required time for visual assessment. To overcome this, our aim is to automate in-utero Carnegie staging using Artificial Intelligence (AI).
Methods
1357 first trimester three-dimensional (3D) ultrasound images of 797 ongoing pregnancies resulting in life birth from The Rotterdam Periconception Cohort were used. We used DenseNet, a state-of-the-art deep learning algorithm for image classification. The algorithm was trained to estimate the Carnegie stage < 16, 16-23, and 23> solely based on the ultrasound images. We used 1100 images of 642 pregnancies for training. For evaluation, we used a test set of 257 images of 155 pregnancies, not used during training.
Results
The AI algorithm achieved an overall accuracy of 61%, which is close to the results of an independent rater, who achieved an accuracy of 63% on 46 images selected for manual VR assessment training. The accuracy was for stage < 16: 55% (n = 9), for stages 16-19: 59% (n = 79), for stages 20-23:62% (n = 151), and for stage >23: 61% (n = 18). The performance differences can partly be explained by the limited size of the embryo early in the first trimester.
Conclusions
Since automatic Carnegie staging using AI is real-time and does not require a VR set-up adoption in clinical practice becomes feasible. In future work, we aim to enhance interpretability by analysing the specific morphological aspects in ultrasound scans utilised by the algorithm to assign the Carnegie stage. Understanding the morphological aspects linked to the Carnegie stage by the algorithm might lead to more in-depth insight into the patterns of normal and abnormal morphological development across pregnancies.
| Original language | English |
|---|---|
| Article number | OP02.01 |
| Pages (from-to) | 61-62 |
| Number of pages | 2 |
| Journal | Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology |
| Volume | 64 |
| Issue number | S1 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 34th ISUOG World Congress on Ultrasound in Obstetrics and Gynecology - Hungexpo Congress and Exhibition Centre, Budapest, Hungary Duration: 15 Sept 2024 → 18 Sept 2024 https://www.isuog.org/events/isuog-world-congress-2024.html |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.