Publications by authors named "Gi Pyo Lee"
Background/objectives: During gastroscopy, accurately identifying the anatomical locations of the gastrointestinal tract is crucial for developing diagnostic aids, such as lesion localization and blind spot alerts.
Methods: This study utilized a dataset of 31,403 still images from 1000 patients with normal findings to annotate the anatomical locations within the images and develop a classification model. The model was then applied to videos of 20 esophagogastroduodenoscopy procedures, where it was validated for real-time location prediction.
Article Synopsis
- A deep learning algorithm was developed to analyze distal radius fractures using X-ray images, and its effectiveness was compared with measurements taken by an orthopedic hand surgeon.
- The study utilized 634 wrist X-ray images, with 507 for training and 127 for testing, to segment the radius and ulna and measure specific radiologic parameters accurately.
- Results showed high accuracy rates (over 99%) for segmentation, and strong correlations (Pearson and intraclass correlation coefficients) between the algorithm's measurements and the manual methods used by the surgeon.
Most of the development of gastric disease prediction models has utilized pre-trained models from natural data, such as ImageNet, which lack knowledge of medical domains. This study proposes Gastro-BaseNet, a classification model trained using gastroscopic image data for abnormal gastric lesions. To prove performance, we compared transfer-learning based on two pre-trained models (Gastro-BaseNet and ImageNet) and two training methods (freeze and fine-tune modes).
View Article and Find Full Text PDFThe primary symptom of both appendicitis and diverticulitis is a pain in the right lower abdomen; it is almost impossible to diagnose these conditions through symptoms alone. However, there will be misdiagnoses happening when using abdominal computed tomography (CT) scans. Most previous studies have used a 3D convolutional neural network (CNN) suitable for processing sequences of images.
View Article and Find Full Text PDFThis study aimed to develop a convolutional neural network (CNN) using the EfficientNet algorithm for the automated classification of acute appendicitis, acute diverticulitis, and normal appendix and to evaluate its diagnostic performance. We retrospectively enrolled 715 patients who underwent contrast-enhanced abdominopelvic computed tomography (CT). Of these, 246 patients had acute appendicitis, 254 had acute diverticulitis, and 215 had normal appendix.
View Article and Find Full Text PDFObjectives: We aim ed to evaluate a commercial artificial intelligence (AI) solution on a multicenter cohort of chest radiographs and to compare physicians' ability to detect and localize referable thoracic abnormalities with and without AI assistance.
Methods: In this retrospective diagnostic cohort study, we investigated 6,006 consecutive patients who underwent both chest radiography and CT. We evaluated a commercially available AI solution intended to facilitate the detection of three chest abnormalities (nodule/masses, consolidation, and pneumothorax) against a reference standard to measure its diagnostic performance.
Article Synopsis
- A retrospective study aimed to create a computer-aided detection (CAD) system that uses deep learning to automate the detection of brain metastases (BMs) and evaluate treatment responses, addressing the need for more efficient assessments in this challenging area.
- The study analyzed 214 MRI exams from 147 patients, separating the data into training, testing, and external validation sets to rigorously assess the CAD's performance against radiologists.
- Results indicated that the CAD system demonstrated varying sensitivity levels (75.1% to 94.7%) and moderate to substantial agreement with the established RANO-BM criteria, showing promise in effectively identifying BMs larger than 5 mm.
Purpose: This study evaluated the performance of a commercially available deep-learning algorithm (DLA) (Insight CXR, Lunit, Seoul, South Korea) for referable thoracic abnormalities on chest X-ray (CXR) using a consecutively collected multicenter health screening cohort.
Methods And Materials: A consecutive health screening cohort of participants who underwent both CXR and chest computed tomography (CT) within 1 month was retrospectively collected from three institutions' health care clinics (n = 5,887). Referable thoracic abnormalities were defined as any radiologic findings requiring further diagnostic evaluation or management, including DLA-target lesions of nodule/mass, consolidation, or pneumothorax.