Orthodontic Treatment and Periodontal Health in the Big Data Era

TY - CHAP

T1 - Orthodontic Treatment and Periodontal Health in the Big Data Era

AU - Jasen, Sara

N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023

Y1 - 2023

N2 - The aim of this research is to conduct a critical analysis review of recent orthodontics literature regarding the interrelationship between orthodontic treatment and periodontal health underlying need for Artificial Intelligence (AI) based algorithms for optimized prediction models and reliable results. After an intensive review of previous publications, 122 research papers were identified and 40 papers were selected according to the inclusion and exclusion criteria. Analysis of the literature revealed that there are discrepancies between results in the published papers. These variations were due to a mermaid of causes such as small sample sizes and inconsistencies in the experimental protocol. The majority of researchers conceptualized and performed orthodontic as well as periodontal treatments differently making it difficult to compare prior findings using standard statistics. This heterogeneity in results leads to difficulties in using the statistical findings in a clinical setting. Furthermore, the majority of studies suffers from low relevance of their statistical findings. Therefore, it is essential to synthesis findings from recent research seeking knowledge gaps and research limitations by critically identifying, highlighting and discussing the gaps and discrepancies that exist in published literature. In the era of AI, big data pooling and optimizing algorithms it is possible to obtain reliable results from multicenter large-scale studies making it possible to generalize findings.

AB - The aim of this research is to conduct a critical analysis review of recent orthodontics literature regarding the interrelationship between orthodontic treatment and periodontal health underlying need for Artificial Intelligence (AI) based algorithms for optimized prediction models and reliable results. After an intensive review of previous publications, 122 research papers were identified and 40 papers were selected according to the inclusion and exclusion criteria. Analysis of the literature revealed that there are discrepancies between results in the published papers. These variations were due to a mermaid of causes such as small sample sizes and inconsistencies in the experimental protocol. The majority of researchers conceptualized and performed orthodontic as well as periodontal treatments differently making it difficult to compare prior findings using standard statistics. This heterogeneity in results leads to difficulties in using the statistical findings in a clinical setting. Furthermore, the majority of studies suffers from low relevance of their statistical findings. Therefore, it is essential to synthesis findings from recent research seeking knowledge gaps and research limitations by critically identifying, highlighting and discussing the gaps and discrepancies that exist in published literature. In the era of AI, big data pooling and optimizing algorithms it is possible to obtain reliable results from multicenter large-scale studies making it possible to generalize findings.

KW - Artificial Intelligence

KW - Deep Learning

KW - Orthodontics

KW - Periodontal Health

UR - http://www.scopus.com/inward/record.url?scp=85169702071&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-42463-2_24

DO - 10.1007/978-3-031-42463-2_24

M3 - Book chapter

AN - SCOPUS:85169702071

SN - 978-3-031-42462-5

T3 - Studies in Big Data

SP - 262

EP - 271

BT - Cutting-Edge Business Technologies in the Big Data Era

PB - Springer

CY - Cham

ER -