ArticleNPJ digital medicine2025
A deep learning based automated maxillary sinus segmentation and bone grafts analysis in CBCT images.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
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Who cites it
4 citing papers in PubMed.
- Letter to the Editor on AI-Driven CBCT Analysis for Surgical Decision by Deng et at.International dental journal · 2026Article
- Automated Classification of Maxillary Sinus Ostium Patency Using a ConvNeXt-Tiny + DeiT Gated MLP-Based Hybrid Deep Learning Model: A Retrospective CBCT Study.Diagnostics (Basel, Switzerland) · 2026Article
- Biomaterial strategies for precision regeneration of the tooth extraction socket: integrating site phenotype, pathological microenvironment, and healing sequence.Frontiers in bioengineering and biotechnology · 2026Review
- A mini-review on the application of AI technology in the segmentation of maxillary sinus for dentistry.Frontiers in surgery · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Precise evaluation of bone gain after maxillary sinus augmentation is critical for optimizing implant therapy yet manual measurements remain time consuming. This study validated a fully automated deep learning system named SA-ai to quantify bone augmentation. A paired CBCT dataset from 85 patients was used to train and test the system which integrates a 2D U-Net for sinus contour and a 3D V-Net for maxilla segmentation. The system achieved a Dice coefficient of 93.2% and registration RMSE of 1.046 mm. Clinical validation against manual measurements showed excellent agreement for bone volume (ICC = 0.993) and other parameters. Bias analysis confirmed measurement stability while workflow efficiency improved over 20-fold compared to manual methods. This registration subtraction paradigm delivers an automated and objective solution for longitudinal monitoring of bone graft volume including one-stage implant cases potentially standardizing clinical evaluation of post augmentation bone dynamics.
Identifiers
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Registered trials
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.