There is a scant literature on the accuracy of dental photographs captured by Digital Single-Lens Reflex (DSLR) and smartphone cameras. The aim was to compare linear measurements of plaster models photographed with DSLR and smartphone's camera with digital models. Thirty maxillary casts were prepared. Vertical and horizontal reference lines were marked on each tooth, with exception to molars. Then, models were scanned with the TRIOS 3 Basic intraoral dental scanner (control). Six photographs were captured for each model: one using DSLR camera (Canon EOS 700D) and five with smartphone (iPhone X) (distance range 16-32 cm). Teeth heights and widths were measured on scans and photographs. The following conclusions could be drawn: (1) the measurements of teeth by means of DSLR and smartphone cameras (at distances of at least 24 cm) and scan did not differ. (2) The measurements of anterior teeth by means of DSLR and smartphone cameras (at all distances tested) and scan exhibited no difference. For documentational purposes, the distortion is negligeable, and both camera devices can be applied. Dentists can rely on DSLR and smartphone cameras (at distances of at least 24 cm) for smile designs providing comparable and reliable linear measurements.
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http://dx.doi.org/10.1155/2021/3910291 | DOI Listing |
Expert Rev Med Devices
December 2024
Mathematical and Computational Sciences Division, Institute of Advanced Study in Science and Technology, Guwahati, India.
Data Brief
August 2024
Department of Ecoscience Aarhus University, C. F. Møllers Allé 8, DK-8000 Aarhus C, Denmark.
The sticky trap is probably the most cost-effective tool for catching insect pests, but the identification and counting of insects on sticky traps is very labour-intensive. When investigating the automatic identification and counting of pests on sticky traps using computer vision and machine learning, two aspects can strongly influence the performance of the model - the colour of the sticky trap and the device used to capture the images of the pests on the sticky trap. As far as we know, there are no available image datasets to study these two aspects in computer vision and deep learning algorithms.
View Article and Find Full Text PDFOral Surg Oral Med Oral Pathol Oral Radiol
November 2024
Hackensack Meridian School of Medicine, Nutley, NJ, USA; Oral and Maxillofacial Surgery, Hackensack University Medical Center, Hackensack, NJ, USA; Rutgers School of Dental Medicine, Newark, NJ, USA; Update Dental College, Dhaka, Bangladesh.
Objective: The purpose of this study was to compare the quality of photographs obtained with 3 different cameras: iPhone, Samsung, and digital single-lens reflex (DSLR), as assessed by oral and maxillofacial surgeons (OMS).
Methods: This was an anonymous online survey study. The study population consisted of OMS in New Jersey, New York, Pennsylvania, and Massachusetts who were members of their state societies.
BMC Oral Health
July 2024
Department of Community Oral Health, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: Dental caries is a global public health concern, and early detection is essential. Traditional methods, particularly visual examination, face access and cost challenges. Teledentistry, as an emerging technology, offers the possibility to overcome such barriers, and it must be given high priority for assessment to optimize the performance of oral healthcare systems.
View Article and Find Full Text PDFSaudi Dent J
May 2024
Department of Pediatric Dentistry, Universitas Indonesia, Jakarta 10430, Indonesia.
Introduction: Dental photography has increasingly been used in practice. One of the purposes of dental photography is for treatment evaluation. Notably, photo resolution affects a picture's quality.
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