Novel Confocal-Laser-Scanning-Microscopy and conventional measures investigating eroded dentine following dentifrice dab-on and brushing abrasion

Contemporary Role and Applications of Artificial Intelligence in Dentistry

Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.

Is whitening toothpaste safe for dental health?: RDA-PE method

The relative dentin abrasivity-profilometry equivalent values were compared using non-contact profilometry with three subtypes of regular toothpaste and two subtypes of whitening toothpaste containing sodium bicarbonate and 35% hydrogen peroxide. Bovine dentin specimens were assigned to six groups: regular toothpaste (R): R1 (BAMBOO SALT GUM OINTMENT); R2 (MEDIAN TARTAR ORIGINAL); R3 (PERIOE Alpha), Reference slurry: RS (calcium pyrophosphate), whitening toothpaste (W): W1 (NET. WT); W2 (Vussen 28 WHITENING). Relative dentin abrasion-profilometry equivalent (RDA-PE) was determined by brushing 10,000 times (n=8). The pH of the toothpaste was measured (n=5) and the abrasive constituents of the toothpaste was analyzed by FE-SEM and EDS. The RDA-PE values ranged from 26 to166, and the pH level ranges were 4.928-9.153. The RDA-PE value of the whitening toothpaste containing hydrogen peroxide was not high compared with that of the regular toothpaste. The RDA-PE values of whitening toothpaste could vary depending on the mechanism and ingredients of the whitening agents.

Measurement of eroded dentine tubule patency and roughness following novel dab-on or brushing abrasion

OBJECTIVES

To investigate the effect of dab-on or brushing of stannous-fluoride SnF2 or sodium-fluoride NaF dentifrice on eroded dentine tubule patency, surface and inter-tubular dentine roughness, using Confocal-Laser-Scanning-Microscopy (CLSM), Atomic-Force-Microscopy (AFM), Energy-Dispersive-X-ray-Spectroscopy (EDX), Scanning-Electron-Microscopy (SEM) and Contact-Profilometry (CP).

METHODS

75-polished human dentine samples were prepared and eroded in agitated 6% citric acid to expose patent tubules and 'initiate' DH. Samples were randomly allocated into 5 intervention groups; artificial saliva control (1); electric tooth-brushing with NaF (2) or SnF2 (3), and dab-on application of NaF (4) or SnF2 (5). Samples underwent three cycles of intervention followed by further acid challenge. Patent tubules, likely to cause DH clinically, were measured using validated biocomputational methods with CLSM images of dentine surfaces taken baseline and post-intervention. Randomised samples (n = 15, 20 %) were investigated using AFM, EDX and SEM to study surface and sub-surface tubular occlusion. Dentine surface and inter-tubular roughness were measured using CP and AFM respectively.

RESULTS

At baseline, mean tubule patency in all samples was 216 (SD 58) with no significant inter-group differences. Post-intervention, the mean patency was 220 (40) and 208 (35) in groups 1 and 2 respectively (p ≥ 0.06), but decreased to 62 (41), 62 (21) and 63 (19) in groups 3, 4 and 5 respectively (p < 0.0001). Patency was confirmed using AFM, SEM and EDX. SnF2 interventions created greater sub-surface occlusion (p < 0.01), and increased CP surface roughness (p = 0.015). Significant negative correlation (-0.6) existed between CP surface roughness and tubule patency (p = 0.009).

CONCLUSIONS

Dab-on with NaF and SnF2 or brushing with SnF2 reduces DH in eroded dentine with ongoing acid challenges. Contacting surface roughness measures indicate risk of DH.

CLINICAL SIGNIFICANCE

Dab-on is a convenient supplementary method of dentifrice application to reduce DH; it beneficially avoids brushing post-erosion or overzealous brushing, enables re-establishment of an appropriate brushing regime post-DH and supports oral health. Significant modes of action of SnF2 in reducing DH are revealed. Finally, CP roughness measures provide indication of dentine lesions that may cause DH clinically.