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James Weiquan Li,Lai Mun Wang,Katsuro Ichimasa,Kenneth Weicong Lin,James Chi-Yong Ngu,Tiing Leong Ang 대한소화기내시경학회 2024 Clinical Endoscopy Vol.57 No.1
The field of artificial intelligence is rapidly evolving, and there has been an interest in its use to predict the risk of lymph node metastasis in T1 colorectal cancer. Accurately predicting lymph node invasion may result in fewer patients undergoing unnecessary surgeries; conversely, inadequate assessments will result in suboptimal oncological outcomes. This narrative review aims to summarize the current literature on deep learning for predicting the probability of lymph node metastasis in T1 colorectal cancer, highlighting areas of potential application and barriers that may limit its generalizability and clinical utility.
Yek Jia Lin Jacklyn,Kiew Sheng Chuu Anne,Ngu James Chi-Yong,Lim Jimmy Guan Cheng 대한마취통증의학회 2020 Korean Journal of Anesthesiology Vol.73 No.6
Background: As the coronavirus disease 2019 (COVID-19) pandemic spreads globally, hospitals are rushing to adapt their facilities, which were not designed to deal with infections adequately. Here, we present the management of a suspected COVID-19 patient. Case: A 66-year-old man with a recent travel history, infective symptoms, and chest X-ray was presented to our hospital. Considering his septic condition, we decided to perform an emergency surgery. The patient was given supplemental oxygen through a face mask and transported to an operating theatre on a plastic-covered trolley. An experienced anesthetist performed rapid sequence intubation using a video laryngoscope. Due to the initial presentation of respiratory distress, the patient remained intubated after surgery to avoid re-intubation. Precautions against droplet, contact, and airborne infection were instituted. Conclusions: Our objective was to facilitate surgical management of patients with known or suspected COVID-19 while minimizing the risk of nosocomial transmission to healthcare workers and other patients.