Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Niha Adnan"'
Autor:
Niha Adnan, Syed Muhammad Faizan Ahmed, Jai Kumar Das, Sehrish Aijaz, Rashna Hoshang Sukhia, Zahra Hoodbhoy, Fahad Umer
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-8 (2024)
Abstract This study evaluates the effectiveness of an Artificial Intelligence (AI)-based smartphone application designed for decay detection on intraoral photographs, comparing its performance to that of junior dentists. Conducted at The Aga Khan Uni
Externí odkaz:
https://doaj.org/article/bc7f1fff279d47fa964751cf80f0d324
Autor:
Fahad Umer, Niha Adnan
Publikováno v:
BDJ Open, Vol 10, Iss 1, Pp 1-5 (2024)
Abstract Introduction Artificial Intelligence (AI) algorithms, particularly Deep Learning (DL) models are known to be data intensive. This has increased the demand for digital data in all domains of healthcare, including dentistry. The main hindrance
Externí odkaz:
https://doaj.org/article/d8f5cf96729641f1a9e613a46e4d0210
Publikováno v:
Journal of the Pakistan Medical Association, Vol 73, Iss 11 (2023)
Periapical diseases ranges from mild granulomatous lesions to large cystic ones, with the treatments corresponding to their respective pre-operative diagnoses. However, the determination of cause of periapical radiolucency is impossible on pre-operat
Externí odkaz:
https://doaj.org/article/00ece297fb954646901596e24d9ac23f
Autor:
Niha Adnan, Fahad Umer
Publikováno v:
Journal of the Pakistan Medical Association, Vol 72, Iss 01 (2022)
The developments in Artificial Intelligence have been on the rise since its advent. The advancements in this field have been the innovative research area across a wide range of industries, making its incorporation in dentistry inevitable. Artificial
Externí odkaz:
https://doaj.org/article/1fda287bb4e1408abf8702cdffea9d44
Publikováno v:
Australian Endodontic Journal.
Publikováno v:
Dentomaxillofac Radiol
Objectives: To investigate the current developments of Artificial Intelligence (AI) in teeth identification on Panoramic Radiographs (PR). Our aim was to evaluate and compare the performances of Deep Learning (DL) models that have been employed in th