ArticleJournal of imaging informatics in medicine2025
A Comparison of Deep Learning vs. Dental Implantologists in Cone-Beam Computed Tomography-Based Bone Quality Classification.
Article in Journal of imaging informatics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Sector Classification of Unerupted Maxillary Canines: A Deep Learning-Based Automated Framework Using Panoramic Radiographs.Orthodontics & craniofacial research · 2026Article
- Retaking assessment system based on the inspiratory state of chest X-ray image.Radiological physics and technology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Bone quality assessment is crucial for pre-surgical implant planning, influencing both implant design and drilling protocol selection. The Lekholm and Zarb (L&Z) classification, which categorizes bone quality into four types based on cortical bone width and trabecular bone density using cone-beam computed tomography (CBCT) data, lacks quantitative guidelines, leading to subjective interpretations. This study aimed to compare the performance of deep learning (DL)-based approaches against human examiners in assessing bone quality, according to the L&Z classification, using CBCT images. A dataset of 1100 CBCT cross-sectional slices was classified into four bone types by two oral and maxillofacial radiologists. Five pre-trained DL models were trained on 1000 images using MATLAB
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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 Socratic graph.