1.Feasibility of a polydioxanone plate as an adjuvant material in rhinoplasty in Asians
Gwang Jin OH ; Jaeik CHOI ; Taek Kyun KIM ; Jae Yong JEONG ; Joo Hak KIM ; Sunje KIM ; Sang Ha OH
Archives of Plastic Surgery 2019;46(2):152-159
BACKGROUND: Nasal framework-supporting procedures such as septal extension grafts, derotation grafts, and columellar strut grafts are usually required in rhinoplasty in Asian patients because the skin envelope is tight, but the nasal framework is small and weak. Autologous materials are preferred, but they have some limitations related to the amount that can be harvested and the frequency of use. Therefore, synthetic materials have been used to overcome these limitations. METHODS: A total of 114 patients who received a polydioxanone (PDS) plate as an adjuvant material in rhinoplasty from September 2016 to August 2017 were retrospectively investigated. The PDS plate was used as to support the weak framework and to correct the contour of the alar cartilages. The PDS plate was used for reinforcement of columellar struts and septal L-struts, alar cartilage push-down grafts, fixation of septal extension grafts, and correction of alar contour deformities RESULTS: Primary and secondary rhinoplasty was performed in 103 and 11 patients, respectively. Clinically, no significant inflammation occurred, but decreased projection of the tip was observed in seven patients and relapse of a short nose was noted in five patients. CONCLUSIONS: PDS plates have been used in the United States and Europe for more than 10 years to provide a scaffold for the nasal framework. These plates can provide reinforcement to columellar struts, L-struts, and septal extension grafts. In addition, they can assist in deformity correction. Therefore, PDS plates can be considered a good adjuvant material for Asian patients with weak and small nasal cartilage.
Asian Continental Ancestry Group
;
Cartilage
;
Congenital Abnormalities
;
Europe
;
Humans
;
Inflammation
;
Nasal Cartilages
;
Nose
;
Polydioxanone
;
Recurrence
;
Retrospective Studies
;
Rhinoplasty
;
Skin
;
Transplants
;
United States
2.Minimally invasive removal of facial foreign body granulomas
Jaeik CHOI ; Geonil KO ; Hyeokjae KWON ; Yooseok HA ; Sunje KIM ; Hyunwoo KYUNG ; Sang-Ha OH ; Seung Han SONG
Archives of Aesthetic Plastic Surgery 2022;28(1):24-30
Background:
Various materials, commonly called fillers, have been developed and are now used for cosmetic and reconstruction purposes. Indiscriminate injections of illegal and unknown substances have various side effects, among which foreign body granulomas are particularly difficult to treat. Surgical resection can be considered for small and well-defined foreign body granulomas, but complete resection is often impossible for wide facial granulomas, and postoperative deformities may occur. Therefore, this study presents cases where foreign bodies were evaluated using imaging studies and removed through minimally invasive procedures depending on their characteristics.
Methods:
Thirty-five patients with chronic granulomas after illegal filler injections treated from 2012 to 2019 were enrolled. Clinically, these granulomas were classified into cystic and infiltrating groups according to the imaging study patterns. Patients in the cystic group underwent puncture and drainage, and those in the infiltrating group were first treated with intralesional laser treatment and then suctioned. If the results were insufficient, surgical removal combined with a lifting procedure was performed.
Results:
All 35 patients were women, and their average age was 51 years. Surgery was successful in almost all cases, but four cases of insufficient removal and contour deformity were encountered during follow-up. Two patients underwent reoperation and two patients improved naturally.
Conclusions
In this study, we classified the characteristics of granulomas using preoperative imaging studies. Aesthetically favorable results were obtained using puncture and drainage and tumescent suction, along with, if necessary, surgical removal accompanied by a lifting procedure when removing facial foreign body granulomas.
3.An Artificial Intelligence-Based Automated Echocardiographic Analysis: Enhancing Efficiency and Prognostic Evaluation in Patients With Revascularized STEMI
Yeonggul JANG ; Hyejung CHOI ; Yeonyee E. YOON ; Jaeik JEON ; Hyejin KIM ; Jiyeon KIM ; Dawun JEONG ; Seongmin HA ; Youngtaek HONG ; Seung-Ah LEE ; Jiesuck PARK ; Wonsuk CHOI ; Hong-Mi CHOI ; In-Chang HWANG ; Goo-Yeong CHO ; Hyuk-Jae CHANG
Korean Circulation Journal 2024;54(11):743-756
Background and Objectives:
Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI).
Methods:
The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI.
Results:
The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements.
Conclusions
Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.
4.An Artificial Intelligence-Based Automated Echocardiographic Analysis: Enhancing Efficiency and Prognostic Evaluation in Patients With Revascularized STEMI
Yeonggul JANG ; Hyejung CHOI ; Yeonyee E. YOON ; Jaeik JEON ; Hyejin KIM ; Jiyeon KIM ; Dawun JEONG ; Seongmin HA ; Youngtaek HONG ; Seung-Ah LEE ; Jiesuck PARK ; Wonsuk CHOI ; Hong-Mi CHOI ; In-Chang HWANG ; Goo-Yeong CHO ; Hyuk-Jae CHANG
Korean Circulation Journal 2024;54(11):743-756
Background and Objectives:
Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI).
Methods:
The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI.
Results:
The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements.
Conclusions
Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.
5.An Artificial Intelligence-Based Automated Echocardiographic Analysis: Enhancing Efficiency and Prognostic Evaluation in Patients With Revascularized STEMI
Yeonggul JANG ; Hyejung CHOI ; Yeonyee E. YOON ; Jaeik JEON ; Hyejin KIM ; Jiyeon KIM ; Dawun JEONG ; Seongmin HA ; Youngtaek HONG ; Seung-Ah LEE ; Jiesuck PARK ; Wonsuk CHOI ; Hong-Mi CHOI ; In-Chang HWANG ; Goo-Yeong CHO ; Hyuk-Jae CHANG
Korean Circulation Journal 2024;54(11):743-756
Background and Objectives:
Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI).
Methods:
The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI.
Results:
The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements.
Conclusions
Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.
6.An Artificial Intelligence-Based Automated Echocardiographic Analysis: Enhancing Efficiency and Prognostic Evaluation in Patients With Revascularized STEMI
Yeonggul JANG ; Hyejung CHOI ; Yeonyee E. YOON ; Jaeik JEON ; Hyejin KIM ; Jiyeon KIM ; Dawun JEONG ; Seongmin HA ; Youngtaek HONG ; Seung-Ah LEE ; Jiesuck PARK ; Wonsuk CHOI ; Hong-Mi CHOI ; In-Chang HWANG ; Goo-Yeong CHO ; Hyuk-Jae CHANG
Korean Circulation Journal 2024;54(11):743-756
Background and Objectives:
Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI).
Methods:
The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI.
Results:
The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements.
Conclusions
Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.