Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Previously, we reported the concept of a cloud-based telemedicine platform for patients with intracerebral hemorrhage (ICH) at local emergency rooms in rural and medically underserved areas in Gangwon state by combining artificial intelligence and remote consultation with a neurosurgeon. Developing a telemedicine ICH treatment protocol exclusively for doctors with less ICH expertise working in emergency rooms should be part of establishing this system. Difficulties arise in providing appropriate early treatment for ICH in rural and underserved areas before the patient is transferred to a nearby hub hospital with stroke specialists. This has been an unmet medical need for decade. The available reporting ICH guidelines are realistically possible in university hospitals with a well-equipped infrastructure. However, it is very difficult for doctors inexperienced with ICH treatment to appropriately select and deliver ICH treatment based on the guidelines. To address these issues, we developed an ICH telemedicine protocol. Neurosurgeons from four university hospitals in Gangwon state first wrote the guidelines, and professors with extensive ICH expertise across the country revised them. Guidelines and recommendations for ICH management were described as simply as possible to allow more doctors to use them easily. We hope that our effort in developing the telemedicine protocols will ultimately improve the quality of ICH treatment in local emergency rooms in rural and underserved areas in Gangwon state.

We aimed to develop a cloud-based telemedicine platform for patients with intracerebral hemorrhage (ICH) at local hospitals in rural and underserved areas in Gangwon-do using artificial intelligence and non-face-to-face collaboration treatment technology. This is a prospective and multi-center development project in which neurosurgeons from four university hospitals in Gangwondo will participate. Information technology experts will verify and improve the performance of the cloud-based telemedicine collaboration platform while treating ICH patients in the actual medical field. Problems identified will be resolved, and the function, performance, security, and safety of the telemedicine platform will be checked through an accredited certification authority. The project will be carried out over 4 years and consists of two phases. The first phase will be from April 2022 to December 2023, and the second phase will be from April 2024 to December 2025. The platform will be developed by dividing the work of the neurosurgeons and information technology experts by setting the order of items through mutual feedback. This article provides information on a project to develop a cloud-based telemedicine platform for acute ICH patients in Gangwon-do.

Purpose: Renal tumors account for approximately 7% of all childhood cancers. These include Wilms tumor (WT), clear cell sarcoma of the kidney (CCSK), malignant rhabdoid tumor of the kidney (MRTK), renal cell carcinoma (RCC), congenital mesoblastic nephroma (CMN) and other rare tumors. We investigated the epidemiology of pediatric renal tumors in Korea. Materials and Methods: From January 2001 to December 2015, data of pediatric patients (0–18 years) newly-diagnosed with renal tumors at 26 hospitals were retrospectively analyzed. Results: Among 439 patients (male, 240), the most common tumor was WT (n=342, 77.9%), followed by RCC (n=36, 8.2%), CCSK (n=24, 5.5%), MRTK (n=16, 3.6%), CMN (n=12, 2.7%), and others (n=9, 2.1%). Median age at diagnosis was 27.1 months (range 0-225.5) and median follow-up duration was 88.5 months (range 0-211.6). Overall, 32 patients died, of whom 17, 11, 1, and 3 died of relapse, progressive disease, second malignant neoplasm, and treatment-related mortality. Five-year overall survival and event free survival were 97.2% and 84.8% in WT, 90.6% and 82.1% in RCC, 81.1% and 63.6% in CCSK, 60.3% and 56.2% in MRTK, and 100% and 91.7% in CMN, respectively (p < 0.001). Conclusion The pediatric renal tumor types in Korea are similar to those previously reported in other countries. WT accounted for a large proportion and survival was excellent. Non-Wilms renal tumors included a variety of tumors and showed inferior outcome, especially MRTK. Further efforts are necessary to optimize the treatment and analyze the genetic characteristics of pediatric renal tumors in Korea.

The first edition of ‘A Standardized Pathology Report for Gastric Cancer’ was initiated by the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists and published 17 years ago. Since then, significant advances have been made in the pathologic diagnosis, molecular genetics, and management of gastric cancer (GC). To reflect those changes, a committee for publishing a second edition of the report was formed within the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists. This second edition consists of two parts: standard data elements and conditional data elements.The standard data elements contain the basic pathologic findings and items necessary to predict the prognosis of GC patients, and they are adequate for routine surgical pathology service. Other diagnostic and prognostic factors relevant to adjuvant therapy, including molecular biomarkers, are classified as conditional data elements to allow each pathologist to selectively choose items appropriate to the environment in their institution. We trust that the standardized pathology report will be helpful for GC diagnosis and facilitate large-scale multidisciplinary collaborative studies.

Background: The risk of device thrombosis and device-oriented clinical outcomes with bioresorbable vascular scaffold (BVS) was reported to be significantly higher than with contemporary drug-eluting stents (DESs). However, optimal device implantation may improve clinical outcomes in patients receiving BVS. The current study evaluated mid-term safety and efficacy of Absorb BVS with meticulous device optimization under intravascular imaging guidance. Methods: The SMART-REWARD and PERSPECTIVE-PCI registries in Korea prospectively enrolled 390 patients with BVS and 675 patients with DES, respectively. The primary endpoint was target vessel failure (TVF) at 2 years and the secondary major endpoint was patientoriented composite outcome (POCO) at 2 years. Results: Patient-level pooled analysis evaluated 1,003 patients (377 patients with BVS and 626 patients with DES). Mean scaffold diameter per lesion was 3.24 ± 0.30 mm in BVS group.Most BVSs were implanted with pre-dilatation (90.9%), intravascular imaging guidance (74.9%), and post-dilatation (73.1%) at proximal to mid segment (81.9%) in target vessel.Patients treated with BVS showed comparable risks of 2-year TVF (2.9% vs. 3.7%, adjusted hazard ratio [HR], 1.283, 95% confidence interval [CI], 0.487–3.378, P = 0.615) and 2-year POCO (4.5% vs. 5.9%, adjusted HR, 1.413, 95% CI, 0.663–3.012,P = 0.370) than those with DES. The rate of 2-year definite or probable device thrombosis (0.3% vs. 0.5%, P = 0.424) was also similar. The sensitivity analyses consistently showed comparable risk of TVF and POCO between the 2 groups. Conclusion With meticulous device optimization under imaging guidance and avoidance of implantation in small vessels, BVS showed comparable risks of 2-year TVF and device thrombosis with DES.

The first edition of ‘A Standardized Pathology Report for Gastric Cancer’ was initiated by the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists and published 17 years ago. Since then, significant advances have been made in the pathologic diagnosis, molecular genetics, and management of gastric cancer (GC). To reflect those changes, a committee for publishing a second edition of the report was formed within the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists. This second edition consists of two parts: standard data elements and conditional data elements. The standard data elements contain the basic pathologic findings and items necessary to predict the prognosis of GC patients, and they are adequate for routine surgical pathology service. Other diagnostic and prognostic factors relevant to adjuvant therapy, including molecular biomarkers, are classified as conditional data elements to allow each pathologist to selectively choose items appropriate to the environment in their institution. We trust that the standardized pathology report will be helpful for GC diagnosis and facilitate large-scale multidisciplinary collaborative studies.

Background: AML is a heterogeneous disease, and despite intensive therapy, recurrence is still high in AML patients who achieve the criterion for cytomorphologic remission (residual tumor burden [measurable residual disease, MRD]<5%). This study aimed to develop a targeted next-generation sequencing (NGS) panel to detect MRD in AML patients and validate its performance. Methods: We designed an error-corrected, targeted MRD-NGS panel without using physical molecular barcodes, including 24 genes. Fifty-four bone marrow and peripheral blood samples from 23 AML patients were sequenced using the panel. The panel design was validated using reference material, and accuracy was assessed using droplet digital PCR. Results: Dilution tests showed excellent linearity and a strong correlation between expected and observed clonal frequencies (R>0.99). The test reproducibly detected MRD in three dilution series samples, with a sensitivity of 0.25% for single-nucleotide variants. More than half of samples from patients with morphologic remission after one month of chemotherapy had detectable mutations. NGS-MRD positivity for samples collected after one month of chemotherapy tended to be associated with poor overall survival and progression-free survival. Conclusions Our highly sensitive and accurate NGS-MRD panel can be readily used to monitor most AML patients in clinical practice, including patients without gene rearrangement. In addition, this NGS-MRD panel may allow the detection of newly emerging clones during clinical relapse, leading to more reliable prognoses of AML.