Minwoo Cho
Seoul National University · Medicine
About the Lab
Professor Minwoo Cho's research lab specializes in medical image analysis and artificial intelligence applications in healthcare, with a focus on enhancing diagnostic accuracy and clinical decision-making through advanced computational techniques. The lab develops AI-driven systems for medical imaging, including computer-aided detection in colonoscopy, super-resolution MRI reconstruction for neurodegenerative disease prediction, and generative models for data augmentation in radiology. Their work also extends to rehabilitation robotics, integrating user-intent detection and motion analysis to improve therapy outcomes in stroke patients.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15OBJECTIVE: Smart hospitals involve the application of recent information and communications technology (ICT) innovations to medical services; however, the concept of a smart hospital has not been rigorously defined. In this study, we aimed to derive the definition and service types of smart hospitals and investigate cases of each type. METHODS: A literature review was conducted regarding the background and technical characteristics of smart hospitals. On this basis, we conducted a focus group in
Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We
The diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.
In the context of stroke rehabilitation, simple structures and user-intent driven actuation are relevant features to facilitate neuroplasticity as well as deliver a sufficient number of repetitions during a single therapy session. A novel robotic treatment device for distal upper limb rehabilitation in stroke patients was developed, and a usability test was performed to assess its clinical feasibility. The rehabilitation robot was designed as a two-axis exoskeleton actuated by electric motors, c
BACKGROUND: Cecal intubation time is an important component for quality colonoscopy. Cecum is the turning point that determines the insertion and withdrawal phase of the colonoscope. For this reason, obtaining information related with location of the cecum in the endoscopic procedure is very useful. Also, it is necessary to detect the direction of colonoscope's movement and time-location of the cecum. METHODS: In order to analysis the direction of scope's movement, the Horn-Schunck algorithm was
BACKGROUND: Accurate interpretation of chest radiographs requires years of medical training, and many countries face a shortage of medical professionals to meet such requirements. Recent advancements in artificial intelligence (AI) have aided diagnoses; however, their performance is often limited due to data imbalance. The aim of this study was to augment imbalanced medical data using generative adversarial networks (GANs) and evaluate the clinical quality of the generated images via a multi-cen
BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia, which makes the lives of patients and their families difficult for various reasons. Therefore, early detection of AD is crucial to alleviating the symptoms through medication and treatment. OBJECTIVE: Given that AD strongly induces language disorders, this study aims to detect AD rapidly by analyzing the language characteristics. MATERIALS AND METHODS: The mini-mental state examination for dementia screening (MMSE-DS), whi
This study developed a new convolutional neural network model to detect and classify gastric lesions as malignant, premalignant, and benign. We used 10,181 white-light endoscopy images from 2606 patients in an 8:1:1 ratio. Lesions were categorized as early gastric cancer (EGC), advanced gastric cancer (AGC), gastric dysplasia, benign gastric ulcer (BGU), benign polyp, and benign erosion. We assessed the lesion detection and classification model using six-class, cancer versus non-cancer, and neop
Objective: Endotracheal intubation (ETI) is critical to secure the airway in emergent situations. Although artificial intelligence algorithms are frequently used to analyze medical images, their application to evaluating intraoral structures based on images captured during emergent ETI remains limited. The aim of this study is to develop an artificial intelligence model for segmenting structures in the oral cavity using video laryngoscope (VL) images. Methods: From 54 VL videos, clinicians manua
Recognizing anatomical sections during colonoscopy is crucial for diagnosing colonic diseases and generating accurate reports. While recent studies have endeavored to identify anatomical regions of the colon using deep learning, the deformable anatomical characteristics of the colon pose challenges for establishing a reliable localization system. This study presents a system utilizing 100 colonoscopy videos, combining density clustering and deep learning. Cascaded CNN models are employed to esti
OBJECTIVE: This study aimed to evaluate the number of laser irradiation sessions, process duration, and hair removal rate required for robot-assisted automatic versus physician-directed laser hair removal. BACKGROUND DATA: This research group previously developed and tested an automatic laser hair removal (LHR) system to provide uniform laser treatment distribution. METHODS: Six subjects 20-40 years of age, with skin types III-IV completed this study. A home-use LHR device with an 810 nm diode l
Robot-assisted surgery platforms are utilized globally thanks to their stereoscopic vision systems and enhanced functional assistance. However, the necessity of ergonomic improvement for their use by surgeons has been increased. In surgical robots, issues with chronic fatigue exist owing to the fixed posture of the conventional stereo viewer (SV) vision system. A head-mounted display was adopted to alleviate the inconvenience, and a virtual vision platform (VVP) is proposed in this study. The VV
Monitoring ankle strength is crucial for assessing daily activities, functional ability, and preventing lower extremity injuries. However, the current methods for measuring ankle strength are often unreliable or not easily portable to be used in clinical settings. Therefore, this study proposes a portable dynamometer with high reliability capable of measuring ankle dorsiflexion and plantar flexion. The proposed portable dynamometer comprised plates made of aluminum alloy 6061 and a miniature ten
Research Areas
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