Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT05090306 |
| Other study ID # |
LIFE |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
November 1, 2020 |
| Est. completion date |
October 31, 2022 |
Study information
| Verified date |
August 2021 |
| Source |
University of Medicine and Pharmacy Craiova |
| Contact |
Dominic G Iliescu, Assoc. Prof. |
| Phone |
723888773 |
| Email |
dominic.iliescu[@]yahoo.com |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
The study to be performed aims to design and develope an automated Intelligent Decision
Support System for fetal echocardiography that can significantly assist the obstetric
physician in the improvement of detection of fetal congenital heart disease compared to the
common standard of care.
Description:
Introduction:
Worldwide, Congenital Heart Disease (CHD) is the most encountered fetal malformation. The
incidence of congenital heart disease appears to be about 1 per 100 live born infants and is
even higher in infants who die before term (1). Fetal echocardiography (FE) has evolved from
just the description of the anatomical abnormalities of the heart toward quantitative
assessment of its function, dimension and shape (2). Presently, FE is performed manually by
the sonographer during the second trimester investigation. However, only half of the babies
undergoing surgery within the first year of life have a prenatal detection (3), explaining
the need for an improvement of the fetal cardiac assessment. Many studies showed the presence
of a significant discrepancy between the pre- and postnatal diagnosis of the CHD obtained by
a manually performed FE (4, 5).
Intelligent Decision Support Systems (ISs) are frameworks that have the capacity to gather
and analyze data, communicate with other systems, learn from experience, and adapt according
to new cases. Technically speaking, ISs are advanced machines that observe and respond to the
environment that they have been exposed to using Artificial Intelligence (AI) (6). This
project aims to foster a cross-fertilization of FE and ISs, which will provide an enormous
potential in developing new fundamental theories and practical methods that rise above the
boundaries of the disciplines involved and lead to new impactful methods that assist medical
practice and discovery.
Methods and analysis:
The study to be performed is a cross-sectional study divided into two separated parts: the
training part of the machine learning approaches within the proposed framework and the
testing phase on previously unseen frames and eventually on actual video scans. All pregnant
women in their first and second trimester are considered eligible for the study. Pregnant
women will be admitted for their routine ultrasound examination and monitoring, first time
between 12-13+6 weeks of pregnancy (for the first trimester anomaly scan) and / or between
18-24 weeks of pregnancy (for the second trimester anomaly scan). Two-dimensional evaluation
of each fetal heart will include a cine loop sweep obtained from the from the four-chamber
view plane by moving the transducer cranially towards the upper mediastinum, allowing the
visualization of the following planes: four-chamber view, left and right ventricular outflow
tracts, three vessels and trachea view. All video files saved from the US devices will be
collected into the cloud. Each ultrasound sweep will be split into frames by the
OB-GYN/Cardio (OBC) department. The Data Science / IT department (DSIT) will process the
frames for obeying the anonymization regulations. For key feature identification, the frames
will be grouped by the OBC into the classes that represent the plane views for each
trimester. DSIT will try different state-of-the-art DL pre-trained algorithms on the data set
with plane views. All the recent DL entries will be tailored and tested on the current two
scenarios of key view identification and semantic segmentation. Their performance results
(their prediction against the ground truth marked by the OBC) will be analyzed in comparison
by means of a statistical test. The accuracy-speed equilibrium will be taken into account in
the ranking of the approaches, since the system will finally perform on a video. Once a new
video will be available in practice, the model chosen for the respective task will highlight
the key feature or the segmented region on video and also provide a degree of confidence in
its recognition. The OBC physicians will validate all the intermediary findings at frame
level, as well as the meaningfulness of the video labelling and segmentation. The outcomes of
the model on the first and second trimester videos of the same patient will be compared to
assess the discrepancy.
AI (ARTIFICIAL INTELLIGENCE) ANALYSIS
Database construction:
The data set for AI analysis will be constructed from images extracted from ultrasound scans
taken in the apical plane. Consequently, a classification problem with four categories
corresponding to the given key views is considered. An additional Other class is appointed
and populated with images of other unimportant frames from the scan.
Image preprocessing for fetal heart scans:
The first step is represented by the extraction performed by converting the image to gray
scale and applying a threshold. In this way, the background noise is discarded. In order to
eliminate the small bridges between the area of interest and other areas in the image,
erosion is applied using a 10x10 pixels kernel. After erosion, besides the area of interest,
several smaller "islands" may appear in the image. The zone of interest has two properties
that distinguish it from the rest of the islands: it always covers the central area of the
image and it has the highest area. In order to eliminate the non-relevant islands, the spots
that cover the central area of the image are first identified, then the spot with the highest
surface coverage is selected. The detected spot is filled, then the dimension is restored by
applying a dilation algorithm, using the same kernel of 10x10 pixels. In order to prevent
losing the fine details around the dilated spot, its convex hull is drawn and filled. In the
end, the generated spot is used as a mask to extract the area of interest from the original
image.
Experimental results:
Three variants of the data collection are considered: the original double-sided (standard +
Doppler) samples, the Doppler single crops and merged image pairs of resulting standard and
Doppler crops.
The ResNet18 and ResNet50 architectures, with similar setups, are applied for each data set
in turn for evaluating the suitability of the preliminary processing.
As the data collections with cropped images contain representatives from all the initial
images, all sets thus have the same number of items and structure per each class, as well as
for the training, validation and test separations. For an objective evaluation, all images
that are extracted from a patient lie within the same separation of the data set, i.e.
training, validation or test. This occurs even if there were multiple video files for a
patient made at different moments (e.g. weeks apart) in time.
The images are resized to 224 × 224 pixels. The 1cycle policy is used and the implementation
is made using fastai and PyTorch libraries. The initial model weights are taken through
transfer learning, as pretrained on the ImageNet data set. The default options for data
augmentations are used. There are 2 steps involved in the training process, each containing
only 10 epochs. Within the first training session, all layers are frozen, except for the
batch normalization layers and the head of the model. Its choice controls how the weights of
the network are adjusted with respect to the loss gradient and selecting a proper value is
essential for making the model converging to a local minimum, and reaching thus improved
accuracy in a smaller amount of epochs. The batch size is taken equal to 32. The model that
hits the highest accuracy on the validation data is applied on the test set. For each of the
2 architectures and for each separate data set, 5 repeated applications are made and the
reported results for each case are computed as the average over the 5 outcomes. The runs are
made in Google Colab, using Tesla T4 for GPU. B. The successful gradient class activation map
(Grad-CAM) approach is used for outlining the decisions of the model for the Aorta and Other
labelling for each data set. The plots are derived from the 5 runs that are computed for each
distinct (architecture, data set) setup.
