Osama Ahmed

Osama Ahmed

, Pune, Maharashtra, India

Profile ID RP-000-7507 Joined 2023-04-22 2 articles

Articles(2)

Authors: Osama Ahmed, Anam Pettiwala

DOI: 10.18231/j.ijpi.2023.005  

Volume8

Issue1

Pages22-27

Abstract

Background: Dental photography is a vital component in the diagnosis and documentation of dental cases. This study aimed to evaluate the familiarity, approach, and practice of dental interns and postgraduate students with respect to dental clinical photography. A total of 250 participants completed a questionnaire, with 78.4% being interns and 21.6% postgraduates. The study found that only 7.3% of participants used a DSLR camera for dental clinical photography, with mobile cameras being the most extensively used device. The high cost of equipment was cited as the primary reason for this. The study also revealed that learning and education needs to be significantly improved in dental clinical photography to ensure better documentation and patient confidentiality. Photography has been widely used in health sciences, including dentistry, to diagnose, document, and record cases. Dental photography is renowned for aiding in clinical diagnosis, similar to the use of x-rays, laboratory workup, or other examinations or tests. However, the use of dental clinical photography varies widely among dental interns and postgraduate students. This study aimed to evaluate their familiarity, approach, and practice in this field. Materials and Methods: A total of 250 participants, including dental interns and postgraduate students, completed a questionnaire about their familiarity, approach, and practice with dental clinical photography. The survey data were collected and analyzed to evaluate the use of different devices, patient consent and confidentiality, and the overall approach of participants towards dental photography. Results: The study found that only 7.3% of participants used a DSLR camera for dental clinical photography, with mobile cameras being the most extensively used device. The high cost of equipment was cited as the primary reason for this. The majority of participants found it essential to document cases for marketing purposes. In terms of maintaining patient consent and confidentiality, informed consent was obtained from the majority of patients before documenting pictures. Additionally, a few of the participants covered their patient’s eyes to avoid breaching confidentiality. Conclusion: The study revealed that learning and education needs to be significantly improved in dental clinical photography to ensure better documentation and patient confidentiality. Dental photography can be made an intriguing subject to record cases efficiently and provide exceptional opportunities for instruction, patient simulation, and recall visits by students. Dental interns and postgraduate students need to be educated on the benefits and correct approach to dental clinical photography. Keywords: Dental photography, Dentistry, DSLR, Diagnosis using photographs

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Authors: Osama Ahmed*, Saleh Abdul Saleem, Aquib Ali Khan, Sana Daruwala, Anam Pettiwala

DOI: 10.18231/j.idjsr.2023.012  

Volume11

Issue2

Pages54-60

Abstract

Revolutionary advancements in dentistry have been brought about by robotic advancements that mimic the functions of the human brain. Over an extended period, both dental and medical documentation has undergone computerization, with cloud servers serving as reliable backups. These records have played a crucial role in identifying victims of major catastrophes such as the World Trade Centre disaster and the Indian Ocean tsunami. Often, in these cases, the bodies are severely damaged and unidentifiable, except for the remaining teeth and the resilient jawbone, which exhibit remarkable resistance to harsh conditions. These elements have proven to be vital in the process of identifying disaster victims. To conduct the present review, extensive online research was performed using databases such as Google Scholar, PubMed, Medline, and IEEE Xplore, up until December 2022.   Keywords: Forensic odontology, Artificial intelligence, Machine learning, Deep learning, Convolution neural network

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