Development of Dysphagia Evaluation Via Video Analysis Based on Deep Learning Method in Neonates and Infants and Correlation Between the Evaluation and the Development

NCT05204966 | Unknown

• 1 other identifier: S2021-1661-0001
• Study Type: interventional
• Target: 50
• Locations: 1 country
• Sites: 1

Brief Summary

The purpose of this study is to develop an evaluation of dysphagia through deep learning-based video analysis in newborns and infants, and to report the correlation with future development.

Conditions

Dysphagia of Newborn

Outcome Measures

Primary Outcomes (2)

• Correlation between NOMAS evaluation and AI analysis of dysphagia

  Correlation between NOMAS evaluation and AI analysis of dysphagia

  up to 2 years

• Correlation between VFSS evaluation and AI analysis of dysphagia

  Correlation between VFSS evaluation and AI analysis of dysphagia

  up to 2 years

Secondary Outcomes (1)

• Correlation between AI analysis of dysphagia and development

  up to 2 years

Study Arms (1)

Infant with suspected dysphagia

OTHER

For newborns and infants who satisfy the inclusion and exclusion criteria, after taking a video of a bottle feeding, we try to develop an evaluation of swallowing disorder through artificial intelligence-based analysis. The developed evaluation will be verified for validity by comparing it with NOMAS (and VFSS if possible), and the correlation with future development will be analyzed through the relationship with the 1st and 2nd year correctional Bailey Developmental Evaluation.

Diagnostic Test: AI analysis

Interventions

AI analysis DIAGNOSTIC_TEST

1. Bottle feeding filming \- With a camera that can measure depth, take a video of feeding milk from a bottle for 5-10 minutes. 2. NOMAS (Neonatal Oral-Motor Assessment Scale) evaluation * Evaluate NOMAS with bottle feeding video by certified evaluators. * NOMAS, developed by Marjorie Meyer Palmer in 1983, is an evaluation method for dysphagia applicable to infants under 48 weeks of PMA. 3. Implementation of video analysis using deep learning (A) Analysis method - Implement a deep learning network that sets keypoints of faces and environments related to swallowing and detects specified keypoints in units of frames. (B) Analyze the movement of key points, and figure out the correlation between swallowing states. (C) Create an input/output process that can automatically classify swallowing states with video as input.

Infant with suspected dysphagia

Eligibility Criteria

• Age: Up to 6 Months
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17)

You may qualify if:

• Newborns and infants under 6 months of correctional age
• Clinically suspected to have dysphagia
• In case parental consent is obtained

You may not qualify if:

• In case of cardiopulmonary instability (ECMO, Ventilator, etc. are being applied)
• When it is not possible to secure sufficient fields for artificial intelligence analysis of images due to factors such as anatomical abnormalities of the head and neck

Sponsors & Collaborators

• Asan Medical Center: lead

Study Sites (1)

Asan medical Center

Seoul, 05505, South Korea

RECRUITING

Central Study Contacts

Eunjae Ko, M.D.

CONTACT

010-9988-6285; gonjae0610@gmail.com

Garam Hong, M.D.

CONTACT

010-9146-7738; eamsmed@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director, Principal investigator, Resident of Physical Medicine and Rehabilitation

Study Record Dates

• First Submitted: January 3, 2022
• First Posted: January 24, 2022
• Study Start: August 2, 2021
• Primary Completion: December 31, 2024
• Study Completion: December 31, 2024
• Last Updated: January 24, 2022

Record last verified: 2022-01

Locations

KR (1)