Evidence map›Paper›PMID 40804594›Full record

ArticleJournal of imaging informatics in medicine2026

Deep Learning-Based Prediction of Orthognathic Surgery Need in Skeletal Class II Patients Using Facial Profile Images: An Artificial Intelligence Study.

Soodeh Tahmasbi, Hadi Shokoofi, Kazem Dalaie, Mohammad Behnaz, Farzad Aghdashi, Farshid Bastami, Asghar Ebadifar, Hoori Mirmohammadsadeghi, Nazila Biglar, Shahab Kavousinejad

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

10 authors.

Soodeh TahmasbiDepartment of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Hadi ShokoofiSchool of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Kazem DalaieDepartment of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mohammad BehnazDepartment of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Farzad AghdashiDepartment of Oral and Maxillofacial Surgery, School of Dentistry, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Farshid BastamiOral and Maxillofacial Surgery Department, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Asghar EbadifarDepartment of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Hoori MirmohammadsadeghiDepartment of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Nazila BiglarDepartment of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Shahab KavousinejadDepartment of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran. dr.shahab.k93@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Orthognathic surgery is often required to address moderate to severe skeletal class II malocclusion, a condition that affects both facial aesthetics and function. Traditional diagnosis relies on cephalometric radiographs and expert evaluation, which can be time-consuming and subject to inter-observer variability. The need for AI-assisted initial screening based exclusively on lateral soft tissue profiles is increasingly recognized, particularly for identifying mandibular retrusion relative to the maxilla. This study aimed to develop and evaluate a deep learning-based model for classifying lateral facial profile photographs into surgical (S) and non-surgical (NS) categories. A dataset of 524 standardized profile images from skeletal class II patients was collected from three dental centers. Each image was independently reviewed by a panel of five experts (three orthodontists and two maxillofacial surgeons), with labels assigned based on majority consensus. To minimize visual bias and enhance model performance, images underwent preprocessing involving facial cropping, background removal via U

Indexed as

Artificial IntelligenceDeep LearningFaceMalocclusion, Angle Class IIOrthognathic Surgical ProceduresCephalometryClassification AlgorithmsConvolutional Neural NetworksFemaleHumansImage Processing, Computer-AssistedMalePhotographyArtificial intelligenceConvolutional neural networkDeep learningFacial profile analysisOrthodonticsOrthognathic surgerySkeletal class II malocclusion

Identifiers

PMID40804594
PMCPMC13230410

What OpenQuestion holds

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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.