Parental Satisfaction from Telemedicine in the Follow-up of Children Operated for Craniosynostosis during COVID-19 Pandemic.

OBJECTIVE: The current study aimed to evaluate the satisfaction level of parents from telemedicine use in the long-term follow-up of children operated for craniosynostosis during the COVID-19 pandemic. DESIGN: A cross-sectional 9-question survey analysis using Qualtrics survey software. SETTING: An institutional study carried out at a national, tertiary level academic center in the Slovak Republic. PATIENTS: All patients operated for non-syndromic and syndromic craniosynostosis in our department, who participated in the virtual postoperative follow-up consultations from March 23, 2020, through July 2, 2021, were included in our series. They were enrolled 498 patients, with the parents of 256 children being responding to the survey. INTERVENTIONS: The survey remained open until July 16, 2021, two weeks after the last virtual consultations. It was delivered to the parents by e-mail to generate closed 5-point Likert scale responses. RESULTS: Overall, 72.3% of parents were satisfied with the telemedicine program, and 67.2% found it convenient. Collectively, 89.1% reported that the received instructions were helpful to them. However, only 18.7% of them answered that they would prefer telemedicine consultations in the future. CONCLUSION: The results of this study showed a high satisfaction from the parents during the virtual follow-up of children operated for craniosynostosis. However, despite their positive experience with telemedicine, the vast majority of parents stated that they would prefer in-person visits in the future.

Artificial Intelligence as an Aid in CBCT Airway Analysis: A Systematic Review.

BACKGROUND: The use of artificial intelligence (AI) in health sciences is becoming increasingly popular among doctors nowadays. This study evaluated the literature regarding the use of AI for CBCT airway analysis. To our knowledge, this is the first systematic review that examines the performance of artificial intelligence in CBCT airway analysis. METHODS: Electronic databases and the reference lists of the relevant research papers were searched for published and unpublished literature. Study selection, data extraction, and risk of bias evaluation were all carried out independently and twice. Finally, five articles were chosen. RESULTS: The results suggested a high correlation between the automatic and manual airway measurements indicating that the airway measurements may be automatically and accurately calculated from CBCT images. CONCLUSIONS: According to the present literature, automatic airway segmentation can be used for clinical purposes. The main key findings of this systematic review are that the automatic airway segmentation is accurate in the measurement of the airway and, at the same time, appears to be fast and easy to use. However, the present literature is really limited, and more studies in the future providing high-quality evidence are needed.