Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

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.

Background: /Aim: Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (EOBMRI) further enhances the identification of additional hepatic nodules compared with computed tomography (CT) alone; however, the optimal treatment for such additional nodules remains unclear. We investigated the long-term oncological effect of aggressive treatment strategies for additional lesions identified using EOB-MRI in patients with hepatocellular carcinoma (HCC). Methods: Data from 522 patients diagnosed with solitary HCC using CT between January 2008 and December 2012 were retrospectively reviewed. Propensity score-matched (PSM) analysis was used to compare the oncologic outcomes between patients with solitary HCC and those with additional nodules on EOB-MRI after aggressive treatment (resection or radiofrequency ablation [RFA]). Results: Among the 383 patients included, 59 had additional nodules identified using EOB-MRI. Compared with patients with solitary HCC, those with additional nodules on EOB-MRI had elevated total bilirubin, aspartate transaminase, and alanine transaminase; had a lower platelet count, higher MELD score, and highly associated with liver cirrhosis (P<0.05). Regarding long-term outcomes, 59 patients with solitary HCC and those with additional nodules after PSM were compared. Disease-free survival (DFS) and overall survival (OS) were comparable between the two groups (DFS, 60.4 vs. 44.3 months, P=0.071; OS, 82.8 vs. 84.8 months, P=0.986). Conclusion The aggressive treatment approach, either resection or RFA, for patients with additional nodules identified on EOBMRI was associated with long-term survival comparable with that for solitary HCC. However, further studies are required to confirm these findings.

Male infertility (MI) and male sexual dysfunction (MSD) can often coexist together due to various interplay factors such as psychosexual, sociocultural and relationship dynamics. The presence of each form of MSD can adversely impact male reproduction and treatment strategies will need to be individualized based on patients’ factors, local expertise, and geographical socioeconomic status. The Asia Pacific Society of Sexual Medicine (APSSM) and the Asian Society of Men’s Health and Aging (ASMHA) aim to provide a consensus statement and practical set of clinical recommendations based on current evidence to guide clinicians in the management of MI and MSD within the Asia-Pacific (AP) region. A comprehensive, narrative review of the literature was performed to identify the various forms of MSD and their association with MI. MEDLINE and EMBASE databases were searched for the following English language articles under the following terms: “low libido”, “erectile dysfunction”, “ejaculatory dysfunction”, “premature ejaculation”, “retrograde ejaculation”, “delayed ejaculation”, “anejaculation”, and “orgasmic dysfunction” between January 2001 to June 2022 with emphasis on published guidelines endorsed by various organizations. This APSSM consensus committee panel evaluated and provided evidence-based recommendations on MI and clinically relevant MSD areas using a modified Delphi method by the panel and specific emphasis on locoregional socioeconomic-cultural issues relevant to the AP region. While variations exist in treatment strategies for managing MI and MSD due to geographical expertise, locoregional resources, and sociocultural factors, the panel agreed that comprehensive fertility evaluation with a multidisciplinary management approach to each MSD domain is recommended. It is important to address individual MI issues with an emphasis on improving spermatogenesis and facilitating reproductive avenues while at the same time, managing various MSD conditions with evidence-based treatments. All therapeutic options should be discussed and implemented based on the patient’s individual needs, beliefs and preferences while incorporating locoregional expertise and available resources.

Purpose: The aim of the present study was to evaluate the per-lesion sensitivity and specificity of the Korean Liver Cancer Association–National Cancer Center (KLCA-NCC) 2022 criteria for the noninvasive diagnosis of hepatocellular carcinoma (HCC), with intraindividual comparison of the diagnostic performance of magnetic resonance imaging with extracellular agents (ECA-MRI) and hepatobiliary agents (HBA-MRI). Materials and Methods: Patients at high risk for HCC who were referred to a tertiary academic institution for hepatic lesions with size ≥ 10 mm between July 2019 and June 2022 were enrolled. A total of 91 patients (mean age, 58.1 years; 76 men and 15 women) with 118 lesions who underwent both ECA-MRI and HBA-MRI were eligible for final analysis. The per-lesion sensitivities and specificities of the KLCA-NCC 2022 criteria using ECA-MRI and HBA-MRI were compared using McNemar’s test. Results: The 118 lesions were 93 HCCs, 4 non-HCC malignancies, and 21 benign lesions. On HBA-MRI, the “definite” HCC category showed significantly higher sensitivity than ECA-MRI (78.5% vs. 58.1%, p < 0.001), with identical specificity (92.0% vs. 92.0%, p > 0.999). For “probable” or “definite” HCC categories, there were no differences in the sensitivity (84.9% vs. 84.9%, p > 0.999) and specificity (84.0% vs. 84.0%, p > 0.999) between ECA-MRI and HBA-MRI. Conclusion The “definite” HCC category of the KLCA-NCC 2022 criteria showed higher sensitivity in diagnosing HCC on HBA-MRI compared with ECA-MRI, without compromising specificity. There were no significant differences in the sensitivity and specificity of “probable” or “definite” HCC categories according to ECA-MRI and HBA-MRI.

Purpose: To compare the level of agreement between the Goldmann applanation tonometer (GAT), iCare IC200 rebound tonometer (IRT), and noncontact tonometer (NCT) in patients who underwent Descemet membrane stripping endothelial keratoplasty (DSEK), and to identify factors contributing to variations in intraocular pressure (IOP) measurements among the three tonometers. Methods: We retrospectively analyzed the medical records of 41 patients who underwent DSEK. IOP was measured using NCT, IRT, and GAT, in this order. We evaluated the level of agreement among IOP measurements using the three tonometers, and analyzed whether clinical factors affected the results. Results: We analyzed 49 eyes of 41 patients (average age: 62.0 years). The IOP values measured by IRT and NCT were lower than those measured by GAT, although the difference was not significant (p = 0.098 and p = 0.320, respectively). A Bland-Altman plot showed greater agreement between IOP measurements obtained by IRT and GAT than those obtained by NCT and GAT. In multivariate regression analysis, the IOP measured by GAT (β = 0.215, p = 0.022), corneal curvature (β = -1.692, p = 0.037), and postoperative duration (β = 0.042, p = 0.018) affected the difference in IOPs measured by GAT and IRT. The IOP measured by GAT (β = 0.301, p = 0.013) and corneal curvature (β = -2.670, p = 0.010) affected the difference in IOP measurements obtained by GAT and NCT. Conclusions In DSEK eyes, IRT showed good agreement and high correlation with GAT, suggesting that it is useful for IOP measurement. However, the IOP measured by GAT, corneal curvature, and postoperative duration should be considered when measuring IOP with an IRT.