eprintid: 8067 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/00/80/67 datestamp: 2023-07-19 23:30:13 lastmod: 2023-07-19 23:30:15 status_changed: 2023-07-19 23:30:13 type: article metadata_visibility: show creators_name: Shafi, Imran creators_name: Fatima, Anum creators_name: Afzal, Hammad creators_name: Díez, Isabel de la Torre creators_name: Lipari, Vivian creators_name: Breñosa, Jose creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: vivian.lipari@uneatlantico.es creators_id: josemanuel.brenosa@uneatlantico.es creators_id: title: A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health ispublished: pub subjects: uneat_bm subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: E-health services; healthcare; deep learning; image processing; medical imaging abstract: Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues. date: 2023-06 publication: Diagnostics volume: 13 number: 13 pagerange: 2196 id_number: doi:10.3390/diagnostics13132196 refereed: TRUE issn: 2075-4418 official_url: http://doi.org/10.3390/diagnostics13132196 access: open language: en citation: Artículo Materias > Biomedicina Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues. metadata Shafi, Imran; Fatima, Anum; Afzal, Hammad; Díez, Isabel de la Torre; Lipari, Vivian; Breñosa, Jose y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, vivian.lipari@uneatlantico.es, josemanuel.brenosa@uneatlantico.es, SIN ESPECIFICAR (2023) A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health. Diagnostics, 13 (13). p. 2196. ISSN 2075-4418 document_url: http://repositorio.unic.co.ao/id/eprint/8067/1/diagnostics-13-02196.pdf