TY  - JOUR
AU  - Khan, Muhammad Salman
AU  - Afridi, Urooj
AU  - Ahmed, Muhammad Jamal
AU  - Zeb, Babar
AU  - Ullah, Irfan
AU  - Hassan, Muhammad Zain
PY  - 2024
DA  - 2024/11/09
TI  - Comprehensive Evaluation of Artificial Intelligence Applications in Forensic Odontology: A Systematic Review and Meta-Analysis
JO  - ICCK Transactions on Intelligent Systematics
T2  - ICCK Transactions on Intelligent Systematics
JF  - ICCK Transactions on Intelligent Systematics
VL  - 1
IS  - 3
SP  - 176
EP  - 189
DO  - 10.62762/TIS.2024.818917
UR  - https://www.icck.org/article/abs/TIS.2024.818917
KW  - artificial intelligence
KW  - forensic odontology
KW  - dental identification
KW  - pattern recognition
KW  - dental identification
AB  - This systematic review and meta-analysis explores the integration of artificial intelligence (AI) technologies into forensic odontology from an intelligent systems perspective, with particular emphasis on enhancing identification accuracy, pattern recognition capabilities, and workflow efficiency. Traditional dental identification methods rely heavily on manual comparison of charts and radiographs, which are time-consuming and susceptible to human bias. Recent advancements in machine learning algorithms, deep learning-based image recognition networks, and intelligent decision-support systems have demonstrated significant potential in automating critical tasks such as bite-mark analysis, dental age estimation, and ante-mortem/post-mortem record reconciliation. Adhering to the PRISMA guidelines, a comprehensive literature search was conducted across PubMed, ScienceDirect, Google Scholar, and Cochrane Library. After removing duplicates and applying pre-established inclusion and exclusion criteria, selected studies were included for qualitative and quantitative synthesis. The analytical performance of these intelligent systems was primarily evaluated using the Kvaal and Cameriere frameworks. The synthesized findings reveal that AI-driven intelligent approaches consistently outperform conventional manual methods in terms of accuracy, speed, and consistency across diverse cohorts. These results underscore the value of intelligent systems as reliable decision-support tools in forensic odontology, paving the way for more robust, efficient, and objective forensic intelligence applications in legal and disaster victim identification contexts.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Khan2024Comprehens,
  author = {Muhammad Salman Khan and Urooj Afridi and Muhammad Jamal Ahmed and Babar Zeb and Irfan Ullah and Muhammad Zain Hassan},
  title = {Comprehensive Evaluation of Artificial Intelligence Applications in Forensic Odontology: A Systematic Review and Meta-Analysis},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2024},
  volume = {1},
  number = {3},
  pages = {176-189},
  doi = {10.62762/TIS.2024.818917},
  url = {https://www.icck.org/article/abs/TIS.2024.818917},
  abstract = {This systematic review and meta-analysis explores the integration of artificial intelligence (AI) technologies into forensic odontology from an intelligent systems perspective, with particular emphasis on enhancing identification accuracy, pattern recognition capabilities, and workflow efficiency. Traditional dental identification methods rely heavily on manual comparison of charts and radiographs, which are time-consuming and susceptible to human bias. Recent advancements in machine learning algorithms, deep learning-based image recognition networks, and intelligent decision-support systems have demonstrated significant potential in automating critical tasks such as bite-mark analysis, dental age estimation, and ante-mortem/post-mortem record reconciliation. Adhering to the PRISMA guidelines, a comprehensive literature search was conducted across PubMed, ScienceDirect, Google Scholar, and Cochrane Library. After removing duplicates and applying pre-established inclusion and exclusion criteria, selected studies were included for qualitative and quantitative synthesis. The analytical performance of these intelligent systems was primarily evaluated using the Kvaal and Cameriere frameworks. The synthesized findings reveal that AI-driven intelligent approaches consistently outperform conventional manual methods in terms of accuracy, speed, and consistency across diverse cohorts. These results underscore the value of intelligent systems as reliable decision-support tools in forensic odontology, paving the way for more robust, efficient, and objective forensic intelligence applications in legal and disaster victim identification contexts.},
  keywords = {artificial intelligence, forensic odontology, dental identification, pattern recognition, dental identification},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}