scholarly journals Comparison of Diagnostic Performance in Thyroid Nodules on US: Deep Convolutional Neural Network Models vs Endocrinologists With Various Experiences

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A859-A859
Author(s):  
Sunyoung Kang ◽  
Eunjung Lee ◽  
Yoo Hyung Kim ◽  
Seul Ki Kwon ◽  
Kyong Yeun Jung ◽  
...  
Keyword(s):  
Neural Network ◽  
Thyroid Cancer ◽  
Convolutional Neural Network ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Deep Convolutional Neural Network ◽  
University Hospital ◽  
Neural Network Models ◽  
Maximal Diameter ◽  
Lower Accuracy

Abstract Objectives: To diagnose thyroid cancer, ultrasonography is a primary tool, but diagnostic accuracy varies according to the proficiency of clinicians. The aim of this study was to compare diagnostic performance between deep convolutional neural network (CNN) models and endocrinologist with various experiences. Methods: Patients who underwent fine needle aspiration at endocrinology department in Seoul National University Hospital, between April 2014 and June 2019, were reviewed. Among them, thyroid nodules which were pathologically confirmed by surgery and maximal diameter greater than 1cm were included. Ultrasonography images of thyroid nodules were reviewed by 13 endocrinologists with various experiences: 0 month (E0, n=8), 1 year (E1, n=2), and >5 years (E5, n=3). Results: Of total 451 thyroid nodules, 66.5% was diagnosed as cancer and 83.7% was papillary thyroid cancer (PTC). Sensitivity and specificity of CNN were 85.3% and 63.6%, respectively, and its area under the curve (AUC) was 0.855. Compared to CNN, mean accuracy of E0 group was significantly lower (Accuracy 68.7% vs 78.0%, P <0.001), and after CNN-assistance, that of E0 was significantly improved (68.7% [before] vs 73.93% [after], P = 0.008). E1 and E5 groups showed similar diagnostic performance to CNN, and CNN-assistance did not change it. Next, subgroup analysis was performed according to the histologic subtypes. AUC of CNN in PTC (0.925) was much higher than that of other cancers including FTC (0.529). Interestingly, CNN-assistance significantly improved diagnostic performance for PTC not only in beginners (E0), but also a subset of experienced endocrinologist (E1 and E5). Conclusions: CNN has good diagnostic performance in the diagnosis of PTC. Endocrinologist with lower experience in ultrasonography, CNN-assistance is beneficial for improving diagnostic performance especially in PTC.

scholarly journals Using the Deep Convolutional Neural Network to Evaluate Thyroid Nodules With Atypia of Undetermined Significance/follicular Lesion of Undetermined Significance Cytology: Multicenter Study

2021 ◽  
Author(s):  
Inyoung Youn ◽  
Eunjung Lee ◽  
Jung Hyun Yoon ◽  
Hye Sun Lee ◽  
Mi-Ri Kwon ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Interobserver Variability ◽  
Needle Aspiration ◽  
Deep Convolutional Neural Network ◽  
Fine Needle ◽  
Experience Levels ◽  
Undetermined Significance

Abstract To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined significance (FLUS) results on fine-needle aspiration (FNA). This study included 202 patients with 202 nodules ≥ 1cm AUS/FLUS on FNA, and underwent surgery in one of 3 different institutions. Diagnostic performances were compared between 8 physicians (4 radiologists, 4 endocrinologists) with varying experience levels and CNN, and AUS/FLUS subgroups were analyzed. Interobserver variability was assessed among the 8 physicians. Of the 202 nodules, 158 were AUS, and 44 were FLUS; 86 were benign, and 116 were malignant. The area under the curves (AUCs) of the 8 physicians and CNN were 0.680-0.722 and 0.666, without significant differences (P > 0.05). In the subgroup analysis, the AUCs for the 8 physicians and CNN were 0.657–0.768 and 0.652 for AUS, 0.469-0.674 and 0.622 for FLUS. Interobserver agreements were moderate (k=0.543), substantial (k=0.652), and moderate (k=0.455) among the 8 physicians, 4 radiologists, and 4 endocrinologists. For thyroid nodules with AUS/FLUS cytology, the diagnostic performance of CNN to differentiate malignancy with US images was comparable to that of physicians with variable experience levels.

scholarly journals Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Inyoung Youn ◽  
Eunjung Lee ◽  
Jung Hyun Yoon ◽  
Hye Sun Lee ◽  
Mi-Ri Kwon ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Interobserver Variability ◽  
Needle Aspiration ◽  
Deep Convolutional Neural Network ◽  
Fine Needle ◽  
Experience Levels ◽  
Undetermined Significance

AbstractTo compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined significance (FLUS) results on fine-needle aspiration (FNA). This study included 202 patients with 202 nodules ≥ 1 cm AUS/FLUS on FNA, and underwent surgery in one of 3 different institutions. Diagnostic performances were compared between 8 physicians (4 radiologists, 4 endocrinologists) with varying experience levels and CNN, and AUS/FLUS subgroups were analyzed. Interobserver variability was assessed among the 8 physicians. Of the 202 nodules, 158 were AUS, and 44 were FLUS; 86 were benign, and 116 were malignant. The area under the curves (AUCs) of the 8 physicians and CNN were 0.680–0.722 and 0.666, without significant differences (P > 0.05). In the subgroup analysis, the AUCs for the 8 physicians and CNN were 0.657–0.768 and 0.652 for AUS, 0.469–0.674 and 0.622 for FLUS. Interobserver agreements were moderate (k = 0.543), substantial (k = 0.652), and moderate (k = 0.455) among the 8 physicians, 4 radiologists, and 4 endocrinologists. For thyroid nodules with AUS/FLUS cytology, the diagnostic performance of CNN to differentiate malignancy with US images was comparable to that of physicians with variable experience levels.

scholarly journals Diagnosis of thyroid nodules on ultrasonography by a deep convolutional neural network

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jieun Koh ◽  
Eunjung Lee ◽  
Kyunghwa Han ◽  
Eun-Kyung Kim ◽  
Eun Ju Son ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Thyroid Nodules ◽  
Medical Center ◽  
Deep Convolutional Neural Network ◽  
University Hospital ◽  
Test Set ◽  
Severance Hospital ◽  
Test Sets ◽  
External Test

Abstract The purpose of this study was to evaluate and compare the diagnostic performances of the deep convolutional neural network (CNN) and expert radiologists for differentiating thyroid nodules on ultrasonography (US), and to validate the results in multicenter data sets. This multicenter retrospective study collected 15,375 US images of thyroid nodules for algorithm development (n = 13,560, Severance Hospital, SH training set), the internal test (n = 634, SH test set), and the external test (n = 781, Samsung Medical Center, SMC set; n = 200, CHA Bundang Medical Center, CBMC set; n = 200, Kyung Hee University Hospital, KUH set). Two individual CNNs and two classification ensembles (CNNE1 and CNNE2) were tested to differentiate malignant and benign thyroid nodules. CNNs demonstrated high area under the curves (AUCs) to diagnose malignant thyroid nodules (0.898–0.937 for the internal test set and 0.821–0.885 for the external test sets). AUC was significantly higher for CNNE2 than radiologists in the SH test set (0.932 vs. 0.840, P < 0.001). AUC was not significantly different between CNNE2 and radiologists in the external test sets (P = 0.113, 0.126, and 0.690). CNN showed diagnostic performances comparable to expert radiologists for differentiating thyroid nodules on US in both the internal and external test sets.

Detection and Classification of Brain Tumors from MRI Images Using a Deep Convolutional Neural Network Approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Cancer Disease

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the fields of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

Sign in / Sign up

Export Citation Format

Share Document