Summary Background Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care professionals in classifying diseases using medical imaging. Methods In this systematic review and meta-analysis, we searched Ovid-MEDLINE, Embase, Science Citation Index, and Conference Proceedings Citation Index for studies published from Jan 1, 2012, to June 6, 2019. Studies comparing the diagnostic performance of deep learning models and health-care professionals based on medical imaging, for any disease, were included. We excluded studies that used medical waveform data graphics material or investigated the accuracy of image segmentation rather than disease classification. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. Studies undertaking an out-of-sample external validation were included in a meta-analysis, using a unified hierarchical model. This study is registered with PROSPERO, CRD42018091176. Findings Our search identified 31 587 studies, of which 82 (describing 147 patient cohorts) were included. 69 studies provided enough data to construct contingency tables, enabling calculation of test accuracy, with sensitivity ranging from 9·7% to 100·0% (mean 79·1%, SD 0·2) and specificity ranging from 38·9% to 100·0% (mean 88·3%, SD 0·1). An out-of-sample external validation was done in 25 studies, of which 14 made the comparison between deep learning models and health-care professionals in the same sample. Comparison of the performance between health-care professionals in these 14 studies, when restricting the analysis to the contingency table for each study reporting the highest accuracy, found a pooled sensitivity of 87·0% (95% CI 83·0–90·2) for deep learning models and 86·4% (79·9–91·0) for health-care professionals, and a pooled specificity of 92·5% (95% CI 85·1–96·4) for deep learning models and 90·5% (80·6–95·7) for health-care professionals. Interpretation Our review found the diagnostic performance of deep learning models to be equivalent to that of health-care professionals. However, a major finding of the review is that few studies presented externally validated results or compared the performance of deep learning models and health-care professionals using the same sample. Additionally, poor reporting is prevalent in deep learning studies, which limits reliable interpretation of the reported diagnostic accuracy. New reporting standards that address specific challenges of deep learning could improve future studies, enabling greater confidence in the results of future evaluations of this promising technology. Funding None.

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

This work provides a systematic review of the literature from January 2003 to April 2014 pertaining to the incidence, pathophysiology, diagnosis, and treatment of osteonecrosis of the jaw (ONJ), and offers recommendations for its management based on multidisciplinary international consensus. ONJ is associated with oncology-dose parenteral antiresorptive therapy of bisphosphonates (BP) and denosumab (Dmab). The incidence of ONJ is greatest in the oncology patient population (1% to 15%), where high doses of these medications are used at frequent intervals. In the osteoporosis patient population, the incidence of ONJ is estimated at 0.001% to 0.01%, marginally higher than the incidence in the general population (<0.001%). New insights into the pathophysiology of ONJ include antiresorptive effects of BPs and Dmab, effects of BPs on gamma delta T-cells and on monocyte and macrophage function, as well as the role of local bacterial infection, inflammation, and necrosis. Advances in imaging include the use of cone beam computerized tomography assessing cortical and cancellous architecture with lower radiation exposure, magnetic resonance imaging, bone scanning, and positron emission tomography, although plain films often suffice. Other risk factors for ONJ include glucocorticoid use, maxillary or mandibular bone surgery, poor oral hygiene, chronic inflammation, diabetes mellitus, ill-fitting dentures, as well as other drugs, including antiangiogenic agents. Prevention strategies for ONJ include elimination or stabilization of oral disease prior to initiation of antiresorptive agents, as well as maintenance of good oral hygiene. In those patients at high risk for the development of ONJ, including cancer patients receiving high-dose BP or Dmab therapy, consideration should be given to withholding antiresorptive therapy following extensive oral surgery until the surgical site heals with mature mucosal coverage. Management of ONJ is based on the stage of the disease, size of the lesions, and the presence of contributing drug therapy and comorbidity. Conservative therapy includes topical antibiotic oral rinses and systemic antibiotic therapy. Localized surgical debridement is indicated in advanced nonresponsive disease and has been successful. Early data have suggested enhanced osseous wound healing with teriparatide in those without contraindications for its use. Experimental therapy includes bone marrow stem cell intralesional transplantation, low-level laser therapy, local platelet-derived growth factor application, hyperbaric oxygen, and tissue grafting.

https://asbmr.onlinelibrary.wiley.com/doi/pdf/10.1002/jbmr.2405

Diagnosis and Management of Osteonecrosis of the Jaw: A Systematic Review and International Consensus

http://www.endoexperience.com/documents/DeVos2009.pdf

Cone-beam computerized tomography (CBCT) imaging of the oral and maxillofacial region: a systematic review of the literature.

Artefacts are common in today's cone beam CT (CBCT). They are induced by discrepancies between the mathematical modelling and the actual physical imaging process. Since artefacts may interfere with the diagnostic process performed on CBCT data sets, every user should be aware of their presence. This article aims to discuss the most prominent artefacts identified in the scientific literature and review the existing knowledge on these artefacts. We also briefly review the basic three-dimensional (3D) reconstruction concept applied by today's CBCT scanners, as all artefacts are more or less directly related to it.

/pdf/artefacts-in-cbct-a-review-20vg8vej3u.pdf

Artefacts in CBCT: a review

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The Essential Physics Of Medical Imaging

Objective Recently, 3-dimensional–computed tomography (3D-CT) imaging has been used in the diagnosis and surgical treatment planning of patients with craniofacial deformities. The present authors have developed a 3D-CT imaging procedure for a 3-dimensional coordinate point evaluation system to assess and diagnose patients with facial asymmetry. Study design The CT data of 16 subjects was selected retrospectively as the control group from patients who had undergone CT examinations to diagnose conditions other than maxillofacial deformities. Anatomical landmarks modified from orthodontic craniometric (cephalometric) points were defined on the 3D-CT images and the asymmetry index of each point was calculated in millimeters. A diagrammatic chart with a baseline indicating the mean asymmetry indices plus the standard deviation in the control group was designed. The resulting diagrammatic chart was used to evaluate the degree of deformity in facial asymmetry patients. Results and conclusions The topography of facial asymmetry was assessed. The 3D-CT imaging technique as described herein is a practical method of evaluating the morphology of facial asymmetry.

3D-CT evaluation of facial asymmetry.

Objective An in vitro study was designed to investigate the influence of projection data discontinuity–related artifacts in limited-volume cone-beam computerized tomography (CBCT) imaging of the jaws. Study design Test objects were positioned in 4 patterns in a water-filled phantom as follows: bimandible and vertebrae, bimandible, left mandible and vertebrae, and left mandible. The CT imaging of the left molar region was performed using image intensifier (II)– and flat panel detector (FPD)–based CBCT scanners. The CT value of the mandible and the adjacent soft tissue region were analyzed for density by means of an 8-bit grayscale. Results The effects of artifacts were scored as the difference in relative density between the lingual and buccal soft tissue. The intensity of artifacts increased when more objects were presented outside the area being imaged. Fewer artifacts were noted in images produced by the particular FPD CBCT used in this investigation. Conclusion The CBCT system using an FPD resulted in fewer artifacts than the CBCT system using an II in this particular study.

Effects of image artifacts on gray-value density in limited-volume cone-beam computerized tomography.

An understanding of the age-related changes in the volume of the normal maxillary sinus will help to identify sinus abnormalities. Normal maxillary sinus volume was measured on axial CT scans in 115 cases, and analysed in relation to age, the presence of premolar and molar teeth, size of the midfacial skeleton and body height and weight. The volume ranged from 4.56-35.21 cm3 (mean: 14.71 +/- s.d. 6.33 cm3) in patients aged over 20 years. There was no significant sex difference and a close correlation between the two sides was found. The volume increased up to the age of 20 years, but then decreased. There was no significant difference in patients with and without maxillary premolars and molars between the ages of 50 and 79 years. The difference in right and left dentitions had no influence on the maxillary sinus volume over the age of 20 years. Adult maxillary sinus volume correlated with the interzygomatic buttress distance. In adult females, there was a significant correlation between the volume and the...

Age changes in the volume of the human maxillary sinus: a study using computed tomography.

Purpose The purpose of this study was to investigate the appearance and possible cause of an artifact seen in limited-volume cone-beam CT imaging. Methods A water-filled plastic cylinder was used as a phantom of the head. A test object was constructed as a bone-equivalent phantom to be imaged. The test object was variously positioned at the center of the phantom and near its margins. CT images of the test object were acquired using a 3DX Accuitomo system. Results In slice images with the test object positioned near the margin of the phantom, arch-shaped defects or deformities were observed on the side of the object. There was a negative correlation between the artifact and the CT value of the object. The artifact was larger in images scanned with a higher voltage. Conclusion The probability that this artifact is caused by halation from the image intensifier (II) system is suggested.

Image artifact in dental cone-beam CT

Purpose Some variations of the mandibular canal (so-called bifid mandibular canal) have been reported using various radiography techniques; however, the occurrences of bifid mandibular canal were less than 1% according to panoramic radiographic surveys. The purpose of the present investigation was to clarify the rate and type of bifid mandibular canal in the mandibular ramus region, as observed using cone-beam computerized tomography (CBCT) images. Materials and methods One hundred twenty-two patients who had undergone preoperative imaging of dental implant treatment using CBCT were enrolled in the investigation. Two-dimensional (2D) images of various planes in the mandibular ramus region were reconstructed on a computer using three-dimensional visualization and measurement software. The course of the mandibular canal was observed and the length of the bifid canal was measured. Results Bifid mandibular canal in the mandibular ramus region was observed in 65% of patients and 43% of sides. Bifid mandibular canal can be classified into four types: retromolar, dental, forward, and buccolingual canals. Conclusion Bifid mandibular canal was observed at a high rate using CBCT.

Eiichiro Ariji

Papers

3D-CT evaluation of facial asymmetry.

Effects of image artifacts on gray-value density in limited-volume cone-beam computerized tomography.

Age changes in the volume of the human maxillary sinus: a study using computed tomography.

Image artifact in dental cone-beam CT

Observation of bifid mandibular canal using cone-beam computerized tomography.