Jiwoo Jang’s research while affiliated with Seoul National University Hospital and other places

This study aims to predict the timing of palatoplasty in infants with cleft lip and palate (CLP) using a machine learning (ML) algorithm. The study included 111 patients with nonsyndromic CLP. Patient subgroups were classified based on birth weight (criterion: -1 SD, low birth weight versus normal birth weight) and cheiloplasty timing (criterion: 5 mo, early cheiloplasty versus late cheiloplasty). Growth trajectories at T2 were compared using T0-weight z -scores between the birth weight subgroups. Changes in the z -scores of weight and height from T1 to T2 were compared between the cheiloplasty timing subgroups. After training the tree-based ML models using cleft type, age, height, and weight at T0, T1, and T2, the palatoplasty timing was predicted with cleft type, weight at T0, and age, height, and weight at T1. The low-birth weight subgroup showed significant catch-up growth during T0-T1 and T0-T2 (all P <0.0001), resulting in no significant difference from the normal birth weight subgroup at T2. Compared with the late cheiloplasty subgroup, the early cheiloplasty subgroup underwent palatoplasty earlier (13.1 versus 14.3 mo; P <0.0001) and showed higher growth rates of weight and height and a greater increase in weight z -scores from T1 to T2 (all P <0.001). The CatBoost algorithm, with a root mean square error of 1.6 months, accurately predicted the palatoplasty timing (mean: actual, 12.8±1.8 mo versus prediction, 12.8±1.0 mo). Use of ML-assisted prediction method may help clinicians decide the timing of personalized palatoplasty in infants with CLP.

Background Light‐emitting diode (LED) light sources have become an increasingly popular choice for the treatment and rejuvenation of various dermatological conditions. Aims This study aimed to evaluate the effects of neck rejuvenation, patient satisfaction, and the safety of LED application to the neck in an Asian population. Methods This was a multicenter, randomized, double‐blind, sham device study. Seventy participants were enrolled in the study. The participants wore the home‐use LED neck device for 9 min a day, 5 times a week, for a total of 60 sessions. The Lemperle Wrinkle Scale (LWS) and Global Aesthetic Improvement Scale (GAIS) were used to evaluate the results of both investigators and participants. The thyroid gland was examined using ultrasonography to evaluate the safety of the investigational device. Results The percentage of participants with improved LWS at Week 12 was significantly higher in the study group. Additionally, the percentage of participants with improved LWS was significantly higher in the study group at Weeks 8, 12, and 16. The LWS at Week 12 corrected with baseline values was found to be significantly different between the two groups. GAIS showed significant differences at 8, 12, and 16 weeks in the investigators' evaluation but not in the participants' evaluation. Repeated‐measures analysis of variance at Weeks 4, 8, 12, and 16 also confirmed a significant difference between the two groups only in investigator assessment. No significant thyroid‐related complications were observed. Conclusion LED application to the neck may be considered a satisfactory and safe procedure for neck rejuvenation.