ABSTRACT
ABSTRACT Objective: To explore the diagnostic value of the TUIAS (SW_TH01/II) computer-aided diagnosis (CAD) software system for the ultrasound Thyroid Imaging Reporting and Data System (TI-RADS) features in thyroid nodules. Materials and methods: This retrospective study enrolled patients with thyroid nodules in Shanghai East Hospital between January 2017 and October 2021. The novel CAD software (SW_TH01/II) and three sonographers performed a qualitative analysis of the ultrasound TI-RADS features in aspect ratio, margin irregularity, margin smoothness, calcification, and echogenicity of the thyroid nodules. Results: A total of 225 patients were enrolled. The accuracy, sensitivity, and specificity of the CAD software in "aspect ratio" were 95.6%, 96.2%, and 95.4%, in "margin irregularity" were 90.7%, 90.5%, and 90.9%, in "margin smoothness" were 85.8%, 88.5%, and 83.0%, in "calcification" were 83.6%, 81.7%, and 82.0%, in "homogeneity" were 88.9%, 90.6%, and 82.2%, in "major echo" were 85.3%, 88.0%, and 85.4%, and in "contains very hypoechoic echo" were 92.0%, 90.0%, and 92.4%. The analysis time of the CAD software was significantly shorter than for the sonographers (2.7 ± 1.6 vs. 29.7 ± 12.7 s, P < 0.001). Conclusion: The CAD system achieved high accuracy in describing thyroid nodule features. It might assist in clinical thyroid nodule analysis.
ABSTRACT
Computer-aided diagnosis (CAD) systems play a very important role in modern medical diagnosis and treatment systems, but their performance is limited by training samples. However, the training samples are affected by factors such as imaging cost, labeling cost and involving patient privacy, resulting in insufficient diversity of training images and difficulty in data obtaining. Therefore, how to efficiently and cost-effectively augment existing medical image datasets has become a research hotspot. In this paper, the research progress on medical image dataset expansion methods is reviewed based on relevant literatures at home and abroad. First, the expansion methods based on geometric transformation and generative adversarial networks are compared and analyzed, and then improvement of the augmentation methods based on generative adversarial networks are emphasized. Finally, some urgent problems in the field of medical image dataset expansion are discussed and the future development trend is prospected.
Subject(s)
Humans , Diagnosis, Computer-Assisted , Diagnostic Imaging , Datasets as TopicABSTRACT
Objective: To evaluate the efficiency of deep-learning based computer aided diagnosis system (DL-CAD) in detecting fractures on DR chest anteroposterior films, and to explore its capability of assisting the junior radiologist. Methods: ①Experiment 1: A total of 547 DR chest anteroposterior films, including 361 patients with 983 chest fractures and 186 without chest fractures were retrospectively analyzed. The predictive performance of DL-CAD for fracture was evaluated. ②Experiment 2: Totally 397 patients were randomly selected from experiment 1, including 211 cases with 604 chest fractures and 186 cases without chest fractures. The results of DL-CAD alone (group 1), a junior radiology resident alone (group 2), a junior radiology resident aided with DL-CAD (group 3) and a senior radiologist alone (group 4) were recorded and compared, respectively. Results: ①For experiment 1: Among 983 fractures, DL-CAD identified 672 fractures, 641 were correctly identified and 31 were misdiagnosed, with a sensitivity of 65.21% (641/983) and F-measure of 77.46%. Out of a total of 361 fracture cases, DL-CAD identified 314 cases, misdiagnosed 6 cases, with a sensitivity of 86.98% (314/361) and F-measure of 92.22%. ②Experiment 2: The sensitivity of fracture detection was 62.09% (375/604), 61.59% (372/604), 86.75% (524/604) and 83.44% (504/604), and the F-measure was 75.38%, 74.62%, 90.74%, 89.84% for group 1, 2, 3 and 4, respectively. The detection efficacy of group 3 and 4 were both higher than that of group 1 and 2 (all P0.05). Conclusion: DL-CAD software showed good detection effect of fractures on DR chest anteroposterior films, which could effectively improve the diagnostic performance of junior radiologist in fracture detection.