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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Pharmacy Reports
Mokhtar, Norehan
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Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Engineering Health Professions Immunology & microbiology Medicine & Pharmacology

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Multi-granularity tooth analysis via YOLO-based object detection models for effective tooth detection and classification AbuSalim, Samah; Zakaria, Nordin; Maqsood, Aarish; Saboor, Abdul; Kwang Hooi, Yew; Mokhtar, Norehan; Jadid Abdulkadir, Said
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2081-2092

Abstract

Effective and intelligent methods to classify medical images, especially in dentistry, can assist in building automated intra-oral healthcare systems. Accurate detection and classification of teeth is the first step in this direction. However, the same class of teeth exhibits significant variations in surface appearance. Moreover, the complex geometrical structure poses challenges in learning discriminative features among the tooth classes. Due to these complex features, tooth classification is one of the challenging research domains in deep learning. To address the aforementioned issues, the presented study proposes discriminative local feature extraction at different granular levels using you only look once (YOLO) models. However, this necessitates a granular intra-oral image dataset. To facilitate this requirement, a dataset at three granular levels (two, four, and seven teeth classes) is developed. YOLOv5, YOLOv6, and YOLOv7 models were trained using 2,790 images. The results indicate superior performance of YOLOv6 for two-class classification achieving a mean average precision (mAP) value of 94%. However, as the granularity level is increased, the performance of YOLO models decreases. For, four and seven-class classification problems, the highest mAP value of 87% and 79% was achieved by YOLOv5 respectively. The results indicate that different levels of granularity play an important role in tooth detection and classification.
In vitro antibiofilm activity of eggshells derived nano-hydroxyapatite (nHA) against Staphylococcus aureus and Streptococcus mutans Suhaimi, Nursyamimi Nasuha; Tarmizi, Nur Hazirah; Zulkifli, Nur Farahim; Amana Allah, Nur Ili Aqilah; Harun, Fakhrul Aimanulhakim; Hanafee, Siti Nur’aisyah Muhamad; Zulkepli, Nur Ayunie; Salim, Fatimah; Mokhtar, Norehan
Pharmacy Reports Vol. 4 No. 3 (2024): Pharmacy Reports
Publisher : Indonesian Young Scientist Group and UPN Veteran Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51511/pr.84

Abstract

Dental caries, a highly prevalent oral health condition worldwide, is primarily driven by the biofilm-forming abilities of Staphylococcus aureus and Streptococcus mutans. The interest in eggshell extracts has grown in recent years due to their potential benefits for oral health. Therefore, this study investigated the potential of nano-hydroxyapatite (nHA) derived from eggshells in combating bacterial infections and inhibiting biofilm formation by the selected cariogenic bacteria. The antibacterial activity of the nano-hydroxyapatite extract was initially assessed using the agar well diffusion method. Subsequently, biofilm inhibition was evaluated through crystal violet assays, and the disruption of biofilm structure was visualized under a light microscope. The findings indicated that the nano-hydroxyapatite extract lacked antibacterial activity in inhibiting the growth of both S. aureus and S. mutans. However, the extract demonstrated antibiofilm activity against mono-species biofilms, with observed disruption of biofilm formation upon treatment. As a result, nano-hydroxyapatite extracts derived from eggshells may hold potential as agents for inhibiting biofilm formation associated with dental caries.
Co-Authors Abdul Saboor AbuSalim, Samah Amana Allah, Nur Ili Aqilah Hanafee, Siti Nur’aisyah Muhamad Harun, Fakhrul Aimanulhakim Jadid Abdulkadir, Said Kwang Hooi, Yew Maqsood, Aarish Salim, Fatimah Suhaimi, Nursyamimi Nasuha Tarmizi, Nur Hazirah Zakaria, Nordin Zulkepli, Nur Ayunie Zulkifli, Nur Farahim