ASSESSMENT AND EVALUATION OF ARTIFICIAL INTELLIGENCE CONTRIBUTION TOWARDS THE EARLY DETECTION AND DIAGNOSIS OF PERIODONTAL DISEASE IN INDIVIDUALS WITH MENTAL HEALTH DISORDERS
Main Article Content
Keywords
Periodontal disease, Artifical Intelligence, Accuracy, Specificity, Sensitivity, Mental Disorders
Abstract
Machine learning (ML) is a crucial component of artificial intelligence (AI), and it's a common misconception that these terms, including deep learning, are interchangeable. The field of medical and dental diagnostics stands to gain significant advantages from this technological advancement. Therefore, a comprehensive understanding of AI and its fundamental components, such as ML, artificial neural networks (ANN), and deep learning (DP), is essential.The objective of this study was to assess the effectiveness of artificial intelligence in both preventing and diagnosing periodontal disease in individuals with mental disorders.This prospective research took place at Muhammad Dental College from February 2023 to July 2023 and involved a total of 527 patients of diverse genders who were diagnosed with mental disorders and presented with complaints related to periodontal disease. Detailed demographic information, including educational and socioeconomic status, was collected for all participants. The study evaluated the sensitivity and specificity of a novel AI system for identifying periodontal disease using intraoral images, adhering to the STARD-2015 statement guidelines for reporting accuracy. Statistical analysis was performed using SPSS 22.0.Among the participants, there were 305 males (57.9%) and 223 females (42.1%), with an average age of 47.5±16.52 years. Of the participants, 295 (55.97%) were smokers. The majority of patients, 325 (61.7%), resided in urban areas, while 202 (38.3%) had rural residences. The most prevalent mental disorders were schizophrenia (50%), depression (29%), and anxiety (13%). Patients with more severe mental disorders, as indicated by the K6 scale index, exhibited a higher prevalence of periodontal disease. Intraoral images accurately detected periodontal disease (gingivitis) with an accuracy ranging between 88%, while AI models for detecting alveolar bone loss achieved an accuracy of 95%. The AI diagnosis had an accuracy rate of 0.90 for identifying healthy pixels and 0.92 for detecting disease pixels.This study underscored that individuals with psychiatric disorders tend to have poorer periodontal health compared to the general population. In addition to providing psychiatric care and therapy, it is advisable to incorporate a targeted preventive dental program. Artificial intelligence demonstrates the potential to identify specific areas with gingival inflammation or periodontal disease, as well as sites without these conditions, with a sensitivity and specificity comparable to those of a visual examination conducted by a human dentist.
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Avinash
Resident Operative Dentistry, Liaquat University of Medical & Health Sciences, Jamshoro, Sindh
Syed Akbar Abbas Zaidi
Assistant Professor, Dental Education Department, Bahria University Medical and Dental College, Karachi
Ashique Hussain Sahito
Assistant Professor, Department of Periodontology, Liaquat University of Medical & Health Sciences, Jamshoro, Sindh
Shahrukh Irfan
Resident Prosthodontic Dentistry, Liaquat University of Medical & Health Sciences, Jamshoro, Sindh
Dinesh Kumar
Resident Oral Pathology, Liaquat University of Medical & Health Sciences, Jamshoro, Sindh
Naina Devi
Houseofficer, Jinnah Postgraduate Medical Center, Karachi, Sindh
Shahzad Baloch
Resident Prosthodontic Dentistry, Liaquat University of Medical and Health Sciences, Jamshoro, Sindh, Pakistan
Ravi Shanker Essarani
Houseofficer, Ziauddin College of Dentistry, Karachi, Sindh
Sorath
Associate Dentist, Abubakar’s Specialist Dental Clinic, Karachi, Sindh
Vijai Nand
Resident Operative Dentistry, Liaquat University of Medical & Health Sciences, Jamshoro, Sindh