ReviewImaging science in dentistry2020
Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: A review.
Review in Imaging science in dentistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled 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.
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
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.
- The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review.Pain research & management · 2021Pooled it
- Hybrid approach with convolutional neural network and regression models for age estimation in panoramic radiographs.International journal of legal medicine · 2026Observational
- Predicting Perceived Profile Attractiveness From Cephalometric Measurements Using Machine Learning.International dental journal · 2026Article
- An Assessment of Deep Learning's Impact on General Dentists' Ability to Detect Alveolar Bone Loss in 2D Intraoral Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses.Bioengineering (Basel, Switzerland) · 2024Review
- Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review.Bioengineering (Basel, Switzerland) · 2024Review
- Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network.Dento maxillo facial radiology · 2024Article
- Detection of the pathological exposure of pulp using an artificial intelligence tool: a multicentric study over periapical radiographs.BMC oral health · 2023Article
- Artificial intelligence in detecting dentomaxillofacial fractures in diagnostic imaging: a scoping review protocol.BMJ open · 2023Article
- A support vector machine-based algorithm to identify bisphosphonate-related osteonecrosis throughout the mandibular bone by using cone beam computerized tomography images.Dento maxillo facial radiology · 2023Article
- Artificial intelligence in early diagnosis and prevention of oral cancer.Asia-Pacific journal of oncology nursing · 2022Review
- Artificial Intelligence: A New Diagnostic Software in Dentistry: A Preliminary Performance Diagnostic Study.International journal of environmental research and public health · 2022Article
- Automatic detection of teeth and dental treatment patterns on dental panoramic radiographs using deep neural networks.Forensic sciences research · 2022Article
- Current applications and development of artificial intelligence for digital dental radiography.Dento maxillo facial radiology · 2022Article
- Ultrasound Imaging versus Radiographs in Differentiating Periapical Lesions: A Systematic Review.Diagnostics (Basel, Switzerland) · 2021Review
- Artificial intelligence in oral and maxillofacial radiology: what is currently possible?Dento maxillo facial radiology · 2021Article
- Web Infrastructure for Data Management, Storage and Computation.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine.Frontiers in oral health · 2021Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intelligent systems (i.e., artificial intelligence), particularly deep learning, are machines able to mimic the cognitive functions of humans to perform tasks of problem-solving and learning. This field deals with computational models that can think and act intelligently, like the human brain, and construct algorithms that can learn from data to make predictions. Artificial intelligence is becoming important in radiology due to its ability to detect abnormalities in radiographic images that are unnoticed by the naked human eye. These systems have reduced radiologists' workload by rapidly recording and presenting data, and thereby monitoring the treatment response with a reduced risk of cognitive bias. Intelligent systems have an important role to play and could be used by dentists as an adjunct to other imaging modalities in making appropriate diagnoses and treatment plans. In the field of maxillofacial radiology, these systems have shown promise for the interpretation of complex images, accurate localization of landmarks, characterization of bone architecture, estimation of oral cancer risk, and the assessment of metastatic lymph nodes, periapical pathologies, and maxillary sinus pathologies. This review discusses the clinical applications and scope of intelligent systems such as machine learning, artificial intelligence, and deep learning programs in maxillofacial imaging.
Indexed as
Identifiers
What OpenQuestion holds
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.