Dental Teleradiology: A Powerful Strategy to Overcome the Impact of COVID-19

Patient preferences for teleradiology services and remote image interpretation: An empirical study

STUDY PURPOSE

This study aims to examine patient preferences for teleradiology services and remote image interpretation. In this context, this study aims to address the following research questions: (i) How do patients perceive teleradiology services, focusing specifically on comfort, quality of care and satisfaction, communication and information, and accessibility and ease of use; (ii) How do patient demographics (age, gender, urban vs. rural residence) influence the perceptions on teleradiology services?

METHODS

A cross-sectional survey design is adopted in this study. The survey comprises five sections targeting demographic information, comfort, and preferences regarding remote image interpretation, perceived quality of care and satisfaction, communication and information clarity, and accessibility and technology aspects using five-point Likert scale ratings. A total of 406 patients (209 males and 197 females; 170 urban residents; 174 semi-urban residents, and 62 rural residents) using teleradiology services participated in the study.

RESULTS

Participants reported high satisfaction with remote image interpretation (3.78 ± 1.19), quality of care (3.31 ± 1.19), understanding (3.84 ± 1.43), and user-friendliness (3.67 ± 1.29). Key issues were technical problems (3.81 ± 1.35), feedback difficulties (3.19 ± 1.58), privacy concerns (2.43 ± 1.46), and low awareness (2.37 ± 1.12). Urban participants scored significantly better in comfort, preferences, and communication than those from semi-urban and rural areas.

CONCLUSION

Teleradiology design and implementation should be optimized to align with patient preferences and enhance overall satisfaction.

DICOM file format has better radiographic image quality than other file formats: an objective study

This study aimed to assess the influence of the file format on the image quality parameters (image noise, brightness, and uniformity) of periapical radiographs acquired with different digital systems. Radiographic images of an acrylic phantom were acquired with two digital systems - Digora Toto and Express, and exported into five different file formats - TIFF, BMP, DICOM, PNG, and JPEG. Image noise, image brightness (mean of gray values), and image uniformity (standard deviation of gray values) were evaluated in all images. A two-way analysis of variance with Tukey's test as a post-hoc test was used to compare the results, considering the file formats and radiographic systems as the studied factors. A significance level of 5% was adopted for all analyses. The DICOM image file format presented lower image noise, higher brightness (higher mean gray values), and greater image uniformity (p<0.001) than the other file formats, which did not differ from each other for both digital radiography systems (p>0.05). The Express system revealed lower image noise and greater image uniformity than the Digora Toto system regardless of the image file format (p<0.001). Moreover, the Express showed higher brightness than the Digora Toto for all image file formats (p<0.001), except for the DICOM image file format, which did not significantly differ between the digital radiography systems tested (p>0.05). The DICOM image file format showed lower image noise, higher brightness, and greater image uniformity than the other file formats (TIFF, BMP, PNG, and JPEG) in both digital radiography systems tested.

A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images

Nearly 3.5 billion humans have oral health issues, including dental caries, which requires dentist-patient exposure in oral examinations. The automated approaches identify and locate carious regions from dental images by localizing and processing either colored photographs or X-ray images taken via specialized dental photography cameras. The dentists' interpretation of carious regions is difficult since the detected regions are masked using solid coloring and limited to a particular dental image type. The software-based automated tools to localize caries from dental images taken via ordinary cameras requires further investigation. This research provided a mixed dataset of dental photographic (colored or X-ray) images, instantiated a deep learning approach to enhance the existing dental image carious regions' localization procedure, and implemented a full-fledged tool to present carious regions via simple dental images automatically. The instantiation mainly exploits the mixed dataset of dental images (colored photographs or X-rays) collected from multiple sources and pre-trained hybrid Mask RCNN to localize dental carious regions. The evaluations performed by the dentists showed that the correctness of annotated datasets is up to 96%, and the accuracy of the proposed system is between 78% and 92%. Moreover, the system achieved the overall satisfaction level of dentists above 80%.

Digital file format does not influence the radiographic diagnosis of vertical root fracture

OBJECTIVE

To evaluate the influence of the file format of digital periapical radiographs on the diagnosis of vertical root fracture (VRF).

STUDY DESIGN

Periapical radiographic images of 34 single-rooted teeth-19 with VRF, and 15 without VRF were acquired using two digital systems-Digora Toto, and Digora Optime, and exported into four different file formats-TIFF, BMP, PNG, and JPEG, totaling 272 radiographs. The radiographs were assessed by five examiners for the detection of VRF, using a 5-point scale (1-definitely absent; 2-probably absent; 3-uncertain; 4-probably present; 5-definitely present). Diagnostic values of area under the ROC curve, specificity, and sensitivity for the diagnosis of VRF were calculated. The results were compared by two-way Analysis of Variance with post hoc Tukey's test. The intra- and inter-examiner agreements were measured by the Kappa test. The significance level was set at 5% for all analyses.

RESULTS

The values of intra-examiner agreement varied from moderate (0.56) to almost perfect (0.81), while the values of inter-examiner agreement varied from fair (0.29) to moderate (0.60). The image file format did not influence the diagnostic values for VRF for any of the radiographic systems tested (p > 0.05). Digora Toto had significantly greater values of area under the ROC curve than Digora Optime for all file formats (p = 0.001).

CONCLUSION

The image file format of periapical radiographs does not influence the diagnosis of VRF, regardless of the digital radiography system.

Recommendations, Practices and Infrastructural Model for the Dental Radiology Set-up in Clinical and Academic Institutions in the COVID-19 Era

The pandemic of Coronavirus disease (COVID-19) has emerged as a global catastrophe that is plaguing mankind. In the past eight months since the world discovered about COVID-19, we learned a lot about server acute respiratory syndrome coronavirus 2 (SARS CoV-2) and perhaps there is much more to discover and understand about the virus. With the current understanding of the disease, we assume it will remain in an active state of transmission and progression among the community for a long time. Thus, it is advisable to adopt the disease's prevention protocol in our daily and work routine. During this pandemic patient requiring dental treatment cannot be neglected and the role of dental imaging is crucial in delivering treatment. Hence, this article attempts to provide an evidence-based compilation about the mode of transmission and clinical features of COVID-19. It also throws light on the potential source of disease transmission in the dental radiology setting. In addition, it suggests preventive measures to curb the infection and infrastructural model of the clinical setting that will assist in achieving control over the disease transmission. This article intends to project a strategy about protocols, infrastructure, and daily activities in a dental radiology office that institutions can adopt with modifications according to their local scenario.