Published online Dec 28, 2023. doi: 10.4329/wjr.v15.i12.359
Peer-review started: October 3, 2023
First decision: October 9, 2023
Revised: November 13, 2023
Accepted: December 5, 2023
Article in press: December 5, 2023
Published online: December 28, 2023
Processing time: 83 Days and 5.1 Hours
Up to 40% of colorectal cancer (CRC) goes undetected on initial computed tomography (CT) scan performed in either the emergency department or outpatient imaging setting. This delay in diagnosis significantly impacts the overall survival of the patients. The ultimate goal is to develop an artificial intelligence (AI)-based second observer for clinical integration so as to improve the clinical diagnosis of CRC on CT studies.
The development of deep learning has shown that AI can potentially serve as a second observer to assist busy radiologist at a reasonable cost, as second reader has been shown in past research to improve imaging diagnosis. However, to develop an AI second observer, large number of training cases with annotated ground truth is required necessitating significant time commitment on the part of the research radiologists.
Our main objective in this research is to compare skip-slice annotation with AI-initiated annotation in time savings for annotating the ground truth for training dataset preparation. Saving annotation time will help improve the efficiency in dataset preparation. Our secondary objective was to evaluate whether ensemble technique could help improve false positive rate for AI-initiated annotation technique. Decreasing false positives per case will make the model more acceptable by clinical radiologist.
The dataset was manually annotated for the entire tumor as well as skipping annotation by one or two slices was measured; 9 total cases were randomly selected to measure the time required to annotate these tumors. These datasets were used to train 2D U-Net model with 5 encoding and 5 decoding layers, using the Adam optimizer. The model accuracy consisting of sensitivity, Dice coefficient estimate, and false positive per case were used to evaluate the model accuracy. The rudimentary AI model was also used to annotate the ground truth; the times required to adjust the annotation for the 9 cases from manually annotation were also measured.
We found that the model trained on skip-slice annotation did not have significant difference in tumor segmentation as a fully annotated dataset and which is statistically significant, thus showing that skip slice annotation can reduce the data preparation time. Although AI-initiated annotation also reduces time, the difference was not statistically significant. Ensemble technique is shown to reduce false positive per case, but at decreased sensitivity.
This study proposes that skip-slice annotation can improve the efficiency in data preparation for AI model training. The significance is that it will reduce the time commitment of highly trained medical personnel in participating in AI medical imaging research.
The future direction of the present research is that this should improve the efficiency in training dataset development given the decreased annotation time.