A review on monitoring orthodontic treatment: from traditional approaches to innovative point-of-care tools

Abstract

Orthodontic treatment is performed to treat malocclusions by improving oral functions, tooth alignment, and facial esthetics that impact the quality of life of patients significantly. Nevertheless, various factors like complexity of malocclusions, patient compliance, and dental hygiene can influence treatment outcomes. Traditional diagnostic methods such as clinical evaluation, radiography techniques, and dental casts have played a major role in orthodontics and have remained a standard diagnostic method in dentistry to date. On the other hand, these methods are associated with major disadvantages like radiation exposure and invasive procedures. To address these limitations, recent diagnostic methods have identified various biomarkers in gingival crevicular fluid and saliva as non-invasive diagnostic methods. These biomarkers can help clinicians in planning orthodontic treatment at the optimal time and determine the appropriate initiation of the treatment. Point-of-care diagnostic tools such as lateral flow assays, Enzyme-Linked Immunosorbent Assay, Clustered Regularly Interspaced Short Palindromic Repeats and microfluidic devices, and wearable technologies are emerging as promising alternatives for orthodontic treatment monitoring by offering rapid, cost-effective, and non-invasive alternatives to traditional methods. Recent developments such as incorporation of artificial intelligence and machine learning into orthodontic software attempt to improve the overall monitoring of orthodontic treatment. This review aims to present various traditional and innovative methods that have shown promising outcomes in research, along with insights into emerging areas that serve as potential game changers in orthodontic treatment monitoring.