Image quality evaluation of eight complementary metal-oxide semiconductor intraoral digital X-ray sensors

Evaluation of Two Dental Digital Imaging Systems Based on Quality Scorings, Burn-Out Effects and Cervical Width Determination

Background/Aim: The aim of the present study is to evaluate diagnostic accuracy of two generic image receptors with CMOS and PSP sensors for image quality scoring (IQS), burnout incidences (BI) and cervical widths (CW) with regard to four different exposure times.

Material and Methods: 43 incisor teeth within 15 paraffin block models were exposed at 4 different exposure times both for the CMOS and PSP groups, and a total number of 120 images were obtained. All images were evaluated by 3 dentomaxillofacial radiologists via 3 different criteria; IQS, BI, CW.

Results: Diagnostic quality scorings between groups displayed statistically significant difference for 0,1; 0,125 and 0,16 sec exposure times. PSP group revealed higher IQS. For 0,125 and 0,16 seconds exposure times, PSP group showed higher percentages of BI. Average CW were lower in PSP group. Although no statistically significant difference was found between average CW vs. exposure times in the CMOS group; PSP group revealed significant differences among exposure times. We can state that, the PSP system displayed higher image quality so exposure times can be reduced, alas, the same conclusion is not possible with CMOS system used.

Conclusions: Image quality perception is higher in PSP system we used, compared with CMOS system. PSP system display more burnout effects with increasing exposure times, while CMOS system is constant.

Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

PURPOSE

Accurate image-based ophthalmic diagnosis relies on fundus image clarity. This has important implications for the quality of ophthalmic diagnoses and for emerging methods such as telemedicine and computer-based image analysis. The purpose of this study was to implement a deep convolutional neural network (CNN) for automated assessment of fundus image quality in retinopathy of prematurity (ROP).

DESIGN

Experimental study.

PARTICIPANTS

Retinal fundus images were collected from preterm infants during routine ROP screenings.

METHODS

Six thousand one hundred thirty-nine retinal fundus images were collected from 9 academic institutions. Each image was graded for quality (acceptable quality [AQ], possibly acceptable quality [PAQ], or not acceptable quality [NAQ]) by 3 independent experts. Quality was defined as the ability to assess an image confidently for the presence of ROP. Of the 6139 images, NAQ, PAQ, and AQ images represented 5.6%, 43.6%, and 50.8% of the image set, respectively. Because of low representation of NAQ images in the data set, images labeled NAQ were grouped into the PAQ category, and a binary CNN classifier was trained using 5-fold cross-validation on 4000 images. A test set of 2109 images was held out for final model evaluation. Additionally, 30 images were ranked from worst to best quality by 6 experts via pairwise comparisons, and the CNN's ability to rank quality, regardless of quality classification, was assessed.

MAIN OUTCOME MEASURES

The CNN performance was evaluated using area under the receiver operating characteristic curve (AUC). A Spearman's rank correlation was calculated to evaluate the overall ability of the CNN to rank images from worst to best quality as compared with experts.

RESULTS

The mean AUC for 5-fold cross-validation was 0.958 (standard deviation, 0.005) for the diagnosis of AQ versus PAQ images. The AUC was 0.965 for the test set. The Spearman's rank correlation coefficient on the set of 30 images was 0.90 as compared with the overall expert consensus ranking.

CONCLUSIONS

This model accurately assessed retinal fundus image quality in a comparable manner with that of experts. This fully automated model has potential for application in clinical settings, telemedicine, and computer-based image analysis in ROP and for generalizability to other ophthalmic diseases.

Deep Learning for Image Quality Assessment of Fundus Images in Retinopathy of Prematurity

Accurate image-based medical diagnosis relies upon adequate image quality and clarity. This has important implications for clinical diagnosis, and for emerging methods such as telemedicine and computer-based image analysis. In this study, we trained a convolutional neural network (CNN) to automatically assess the quality of retinal fundus images in a representative ophthalmic disease, retinopathy of prematurity (ROP). 6,043 wide-angle fundus images were collected from preterm infants during routine ROP screening examinations. Images were assessed by clinical experts for quality regarding ability to diagnose ROP accurately, and were labeled "acceptable" or "not acceptable." The CNN training, validation and test sets consisted of 2,770 images, 200 images, and 3,073 images, respectively. Test set accuracy was 89.1%, with area under the receiver operating curve equal to 0.964, and area under the precision-recall curve equal to 0.966. Taken together, our CNN shows promise as a useful prescreening method for telemedicine and computer-based image analysis applications. We feel this methodology is generalizable to all clinical domains involving image-based diagnosis.

Application of portable digital radiography for dental investigations of ancient Egyptian mummies during archaeological excavations: Evaluation and discussion of the advantages and limitations of different approaches and projections

Purpose

In the age of X-ray computed tomography (CT) and digital volume tomography (DVT), with their outstanding post-processing capabilities, indications for planar radiography for the study of the dentition of ancient Egyptian mummies may easily be overlooked. In this article, the advantages and limitations of different approaches and projections are discussed for planar oral and maxillofacial radiography using portable digital X-ray equipment during archaeological excavations. Furthermore, recommendations are provided regarding projections and sample positioning in this context.

Materials and Methods

A total of 55 specimens, including 19 skeletonized mandibles, 14 skeletonized skulls, 18 separate mummified heads, and 4 partially preserved mummies were imaged using portable digital X-ray equipment in the course of archaeological excavations led by the University of Basel in the Valley of the Kings between 2009 and 2012. Images were evaluated by 2 authors with regard to the visibility of diagnostically relevant dental structures using a 4-point grading system (Likert scale).

Results

Overall, the visibility of diagnostically relevant dental structures was rated highest by both authors on X-ray images acquired using a dental detector. The tube-shift technique in the lateral projections of mandibular dentition achieved the second-best rating, and lateral projections achieved the third-best rating.

Conclusion

Conventional planar digital X-ray imaging, due to its ubiquity, remains an excellent method–and often the only practicable one–for examining the skulls and teeth of ancient Egyptian mummies under field conditions. Radiographic images of excellent diagnostic quality can be obtained, if an appropriate methodology regarding the selected projections and sample placement is followed.