Evidence map›Paper›PMID 39306752›Full record

ArticleOrthodontics & craniofacial research2025

Smartphone-based scans of palate models of newborns with cleft lip and palate: Outlooks for three-dimensional image capturing and machine learning plate tool.

José Wittor de Macêdo Santos, Andreas Albert Mueller, Benito K Benitez, Yoriko Lill, Prasad Nalabothu, Francisco Wilker Mustafa Gomes Muniz

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Orthodontics & craniofacial research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Smartphone and AI Workflow for 3D Printed Plate for Presurgical Therapy in Cleft Lip and Palate: Retrospective Evaluation of Outcomes.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2026
    Article
  3. Review
  4. Article
  5. Article
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

José Wittor de Macêdo SantosDepartment of Oral and Maxillofacial Surgery and Maxillofacial Prosthodontics, School of Dentistry, Federal University of Pelotas, Pelotas, Rio Grande do Sul, Brazil.
Andreas Albert MuellerDepartment of Oral and Craniomaxillofacial Surgery, University Hospital Basel and University Children's Hospital Basel, Basel, Switzerland.
Benito K BenitezDepartment of Oral and Craniomaxillofacial Surgery, University Hospital Basel and University Children's Hospital Basel, Basel, Switzerland.
Yoriko LillFacial and Cranial Anomalies Research Group, Department of Biomedical Engineering and Department of Clinical Research, University of Basel, Basel, Switzerland.
Prasad NalabothuDepartment of Oral and Craniomaxillofacial Surgery, University Hospital Basel and University Children's Hospital Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0003-2442-7618
Francisco Wilker Mustafa Gomes MunizDepartment of Periodontology, School of Dentistry, Federal University of Pelotas, Pelotas, Rio Grande do Sul, Brazil.ORCID https://orcid.org/0000-0002-3945-1752

Funding

Botnar Research Centre for Child Health, University of BaselCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorSwiss National Science Foundation 205321_205008Universität Basel
6 · The paper itself

Abstract

objectivesTo evaluate the performance of smartphone scanning applications (apps) in acquiring 3D meshes of cleft palate models. Secondarily, to validate a machine learning (ML) tool for computing automated presurgical plate (PSP). MATERIALS AND

methodsWe conducted a comparative analysis of two apps on 15 cleft palate models: five unilateral cleft lip and palate (UCLP), five bilateral cleft lip and palate (BCLP) and five isolated cleft palate (ICP). The scans were performed with and without a mirror to simulate intraoral acquisition. The 3D reconstructions were compared to control reconstructions acquired using a professional intraoral scanner using open-source software.

resultsThirty 3D scans were acquired by each app, totalling 60 scans. The main findings were in the UCLP sample, where the KIRI scans without a mirror (0.22 ± 0.03 mm) had a good performance with a deviation from the ground truth comparable to the control group (0.14 ± 0.13 mm) (p = .653). Scaniverse scans with a mirror showed the lowest accuracy of all the samples. The ML tool was able to predict the landmarks and automatically generate the plates, except in ICP models. KIRI scans' plates showed better performance with (0.22 ± 0.06 mm) and without mirror (0.18 ± 0.05 mm), being comparable with controls (0.16 ± 0.08 mm) (p = .954 and p = .439, respectively).

conclusionsKIRI Engine performed better in scanning UCLP models without a mirror. The ML tool showed a high capability for morphology recognition and automated PSP generation.

Indexed as

Cleft LipCleft PalateImaging, Three-DimensionalMachine LearningSmartphoneHumansInfant, NewbornMobile ApplicationsModels, Anatomiccleft palatecomputer simulationsdental technologymorphologysmartphone apps

Identifiers

PMID39306752
PMCPMC11701956

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.