This observational study investigates the impact of passive plate therapy using an
automated, artificial intelligence (AI)-driven design workflow on presurgical cleft size
in infants with unilateral cleft lip and palate. Cleft lip and palate represent one of
the most common congenital craniofacial anomalies, and early presurgical interventions
such as passive plates aim to reduce the cleft size, support feeding, and facilitate
better surgical outcomes. However, access to such interventions is often limited by the
need for specialized staff, complex workflows, and reliance on intraoral impressions.
Recent technological advancements have enabled the integration of digital workflows and
AI into presurgical cleft care. In particular, a pipeline has been developed that uses
intraoral scanning, and AI-assisted modeling to design individualized passive plates.
These plates are manufactured via 3D printing and do not require invasive impressions nor
extensive laboratory work making them potentially safer and more scalable in low or
medium resource settings.
This study specifically evaluates the clinical effectiveness of such AI-designed passive
plates compared to standard care without any presurgical orthopedic therapy. Infants are
enrolled from two sites in India (Chennai and Hyderabad), where clinical practices differ
with respect to the use of passive plates. As this is a non-randomized, observational
study, group assignment is based on local standard of care at each site.
The primary objective is to assess the percentage reduction in cleft width from birth to
the time of primary surgery (typically around 4 months of age) in infants treated with
AI-designed passive plates. The secondary objective is to compare cleft width at the time
of surgery between infants who received the plates and those who did not, offering
insight into the relative anatomical outcomes of the intervention.
All participating infants will undergo standardized intraoral scans at baseline (within
the first two weeks of life) and again just prior to primary surgical repair. The scans
are used to measure the anterior-posterior cleft width and calculate percentage change
over time. Cleft measurements are obtained digitally from 3D scan data using validated
image processing software.
The AI-assisted design of the passive plates is performed using a custom plugin within
Blender software, which automatically detects anatomical landmarks and generates the
plate geometry with minimal user input. The digital files are subsequently exported for
3D printing using biocompatible materials. Infants in the intervention group will wear
the plate continuously from the time of fitting until primary surgery, under the
supervision of trained clinical teams.
Data from the two cohorts (plate vs. no plate) will be compared using appropriate
statistical methods to assess differences in cleft size reduction and absolute cleft
width at surgery. This study does not include randomization or blinding, as the
intervention is assigned based on institutional practice. However, efforts will be made
to ensure consistency in scanning methods, measurement protocols, and outcome assessment
across both sites.
This study is part of a larger initiative to evaluate the generalizability and
effectiveness of AI-based digital workflows in cleft care across diverse healthcare
settings. The findings are expected to inform future integration of automated design
technologies in presurgical treatment planning, especially in regions where access to
skilled cleft teams is limited.
No additional interventions, medications, or behavioral changes are introduced as part of
this study. Participation involves routine procedures and follow-up through the standard
cleft treatment timeline.
