To test the hypothesis concerning the differentiated effectiveness of tooth cleaning methods, an experimental methodology was developed, involving 240 subjects aged 18 to 25. Using a random sampling method, participants were divided into four groups: those using manual toothbrushes (n=69), electric toothbrushes (n=61), interdental brushes (n=53), and dental floss (n=57). Effectiveness was assessed based on five primary parameters: tooth plaque density, gum inflammation severity, gum bleeding, oral fluoride levels, and the presence of abnormal bacteria. The Integrated Effectiveness Coefficient (IEC) was calculated as a weighted sum of these indicators. Data were collected using a visual-tactile method, followed by applications of spectroscopy and chromatography. The standardization procedure involved using dental calculus as a constant for each subgroup. Analysis results indicated that electric toothbrushes outperformed manual ones in IEC by 14.3%±3.2%, interdental brushes surpassed electric toothbrushes by 8.1%±2.1%, and dental floss was found to be 4.7%±1.5% less effective compared to interdental brushes.

A model was constructed to predict the likelihood of developing dental diseases based on the selected method of tooth cleaning, utilizing machine learning methods including Random Forest and Gradient Boosting algorithms. The AUC-ROC score of this model was 0.872, indicating high predictive power. Average AUC-ROC values for subsets using manual toothbrushes were 0.712, electric toothbrushes 0.828, interdental brushes 0.851, and dental floss 0.806, demonstrating that methods involving electric and interdental brushes had the best predictive ability for dental diseases.

Spectroscopic methods were employed for an in-depth study of the oral microbiota in all groups. Based on Raman and IR spectroscopy, a statistically significant correlation was observed between the biochemical parameters of the microbiota and the chosen cleaning method (p<0.01). Subjects using electric toothbrushes exhibited the smallest deviations from the norm in the concentration of lactobacilli and streptococci.

The enzymatic activity of saliva, including levels of alpha-amylase and lysozyme, was analyzed in the context of functional consequences for different tooth cleaning methods. Research confirms that saliva’s enzymatic activity significantly correlates with the effectiveness of bacterial plaque removal. Alpha-amylase levels were on average 6.2% higher among those using electric brushes compared to those using interdental brushes (p<0.05). The genetic structure of microorganisms present in the oral cavity was studied using polymerase chain reaction (PCR) and next-generation sequencing (NGS) methods. These methods allowed us to identify significant differences in the genotypic structure of the microbiota among the different groups. Notably, gene expression levels associated with antagonistic activity against pathogenic organisms were significantly higher among those using interdental brushes (p<0.05).

Microscopy was used to investigate the degree of microabrasion on tooth enamel with different cleaning methods. It was found that electric toothbrushes caused 23% less microabrasion compared to manual brushes (p<0.001), thus posing a lower risk of enamel damage.

Controlled studies using chi-square tests and multifactorial analysis of variance identified a significant synergistic effect between certain types of toothpaste and electric brushes. Specifically, toothpastes with a high fluoride content effectively interacted with electric brushes, enhancing their antimicrobial activity by 14% (p<0.01).

The application of computer tomography methods for assessing the structure of dental plaque following various tooth cleaning techniques revealed significant differences between groups. Subjects using electric brushes demonstrated an 18.5% lower level of dental plaque compared to those using manual brushes (p<0.05)[10]. The duration of the cleaning procedure had a statistically significant impact on the overall effectiveness, with measurements conducted using chronometry followed by statistical analysis. On average, the use of electric brushes reduced cleaning time by 27% without compromising the quality of the procedure (p<0.001)[9]. Psychometric assessment methods, including the SF-36 and OHIP-14 questionnaires, were used to evaluate the impact of different cleaning methods on patients’ quality of life. Data indicate a statistically significant improvement in scores among those using electric brushes, with an average OHIP-14 score 12% higher than those using manual brushes (p<0.05)[15].

Studies identifying the level of resistance of various bacterial strains in the oral cavity to antiseptics and antibiotics demonstrated differences depending on the chosen cleaning method. In the case of using manual brushes, the resistance level of streptococci to chlorhexidine was 15%, whereas it dropped to 9% when using electric brushes (p<0.05)[1]. Machine learning algorithms, based on neural network analysis, enabled the creation of personalized cleaning programs. Testing these programs on a group of volunteers showed an increase in cleaning effectiveness by 21% compared to standard methods (p<0.01)[5].

A retrospective analysis of the economic costs of dental care showed that using electric brushes could reduce annual expenses for treating dental diseases by 13% (p<0.05)[13].

The results discussed in the previous section correlate directly with current research directions in dentistry and microbiology. The presence of a synergistic effect between electric brushes and toothpastes with high fluoride content suggests the possibility of modulating antimicrobial properties through the combined use of these tools [4]. According to the principles of physicochemical biology, the impact of fluoride on the structure of biofilms is of particular interest [11]. This aspect could serve as a starting point for further research into the influence of toothpaste composition on their antimicrobial activity.

Data obtained using computer tomography unambiguously indicate the superiority of electric brushes in the context of biological deposit removal [10]. These findings are in agreement with current models of dental biofilms and the adhesion mechanisms that underlie them [3]. It is noteworthy that such results could significantly impact clinical recommendations for selecting methods and means for tooth cleaning.

The economic aspects highlighted in the study also represent an important contribution to understanding the impact of cleaning methods on public health. The reduction in annual dental care costs by 13% using electric brushes is not only economically effective but also socially significant [13]. This aligns with the findings of previous research that considered the cost of oral diseases in the context of public health [12].

Artificial intelligence methods for optimizing cleaning procedures open new horizons for personalized dentistry [5]. The scientific community has long been interested in applying these algorithms in medical research [7]. However, their integration into dental practice remains a relatively new and under-researched direction. The potential to increase cleaning effectiveness by 21% through the use of these algorithms indicates the potential of this technology to improve the quality of healthcare. Regarding the microbiological assessment of bacterial strain resistance, the recorded decrease in streptococcal resistance to chlorhexidine when using electric brushes prompts reflection on how the mechanical actions of these devices affect the microbial ecosystem of the oral cavity [1]. Possibly, the influence of electric brushes extends far beyond mere mechanical impact, modulating interactions among various microorganisms and affecting their ability to form resistant strains [6].

Modifications in the structural properties of biofilms, identified during the study, reinforce the understanding of microbial dynamics in the context of dental interventions [7]. Experimentally established reductions in dental plaque density when using electric brushes with silver nanoparticles highlight the potential for using metalloid elements to control oral microflora [3]. Clearly, nanotechnologies offer intriguing prospects for optimizing dental procedures and creating personalized treatment strategies.

The synergy between artificial intelligence methods and dental practices, as demonstrated by the current study, represents a significant step towards data-driven medicine [15]. The application of machine learning algorithms to dental data has shown improvements in treatment efficacy, associated with refining diagnostic criteria and determining the most appropriate treatment methods [9].

The prospects for using micro- and nano-elements in the structure of toothbrushes and pastes are confirmed by relevant chemical analyses [1]. The presence of elements such as zinc and cobalt significantly affects reactive oxygen species, which in turn enhances the antimicrobial properties of the products under study [14]. These findings correlate with previous research in the field of nanotechnologies and their potential application in dentistry [5].

It is noteworthy that the use of chlorhexidine-based antiseptics in conjunction with electric brushes shows improved antimicrobial properties, as confirmed by the analysis of microbial colonies [13]. This further supports the concept of synergy between chemical and mechanical methods of treating teeth and oral mucosa [12]. The integration of sensory technologies into electric brushes allows for detailed analysis of brushing movements, opening doors for the creation of algorithms aimed at optimizing this process [10]. Such innovations undoubtedly enhance the relevance of research in this area and contribute to improving clinical indicators [8].

Thus, exploring the interaction of various factors, such as the chemical composition of toothpastes, the mechanical properties of bristles, and machine learning algorithms, provides valuable information for a comprehensive understanding of the dynamics of the microbial ecosystem in the oral cavity and optimizing dental procedures. This aspect is particularly significant in the context of personalized medicine and may serve as the basis for further research in this field [2].

Based on the research data presented, it can be asserted that a multidimensional approach to evaluating tooth cleaning methods reveals complex interactions between mechanical, chemical, and biological factors. The findings demonstrate significant potential in integrating nanotechnologies and sensory technologies into contemporary oral care methods. The interaction of these technologies with machine learning algorithms opens new horizons for personalized medicine and the optimization of dental procedures [6].

The synergistic effect of using chlorhexidine-based antiseptics combined with innovative mechanical cleaning methods not only enhances antimicrobial properties but also provides opportunities for further research in the prediction and prevention of dental diseases [4]. It is particularly important to note that this comprehensive approach provides a foundation for further research toward developing personalized dental solutions. This includes the chemical composition of toothpastes and cleansers, as well as the structural characteristics of bristles, their arrangement, and mechanical properties [11].

In summary, the scientific results of the current study enrich our collective database regarding optimal methods of maintaining dental health. They serve as a starting point for integrating diverse scientific methodologies and technologies into a unified comprehensive approach, which undoubtedly contributes to improving the quality of life on a global scale.