Revista Latinoamericana de la Papa

Tag: Deep Learning

  • REVOLUTIONISING DENTAL CARE THROUGH ARTIFICIAL INTELLIGENCE: APPLICATIONS IN ORAL SURGERY AND DIAGNOSTICS

    Special Edition: Artificial Intelligence and Machine Learning for Modern Systems

    Original Research Article

    Authors: Er. Rishabh Aryan1*, Prof. Dr. Tryambak Hiwarkar2

    1M.Tech (Artificial Intelligence and Data Science), Department of Computer Science and Engineering,

    Indian Institute of Information Technology, Bhagalpur (Bihar), India

    2Director, ASM Group of Institutions, Pune, Maharashtra, India

    E-mail: rishabh.250201011@iiitbh.ac.in  |  tryambakhiwarkar@asmedu.org

    *Corresponding author: rishabh.250201011@iiitbh.ac.in

    Abstract

    Artificial Intelligence (AI) is catalysing a paradigm shift in dental medicine, particularly in the domains of oral surgery and clinical diagnostics. This original research study presents a prospective, multi-centre evaluation of AI-assisted diagnostic and surgical-planning systems applied to 1,840 dental patients across four tertiary care centres between January 2022 and December 2024. Employing convolutional neural networks (CNN), residual deep learning (ResNet-50), long short-term memory (LSTM) architectures, and support vector machines (SVM), the integrated AI platform demonstrated diagnostic accuracy of 94.2% for carious lesion detection, 91.8% for periapical pathology, 93.5% for early-stage oral mucosal cancer screening, and 89.4% for implant site assessment — all significantly exceeding mean clinician baselines (p < 0.001). AI-assisted surgical planning reduced pre-operative planning time by 73.6% for implant placement and 71.1% for orthognathic surgery procedures. Patient-reported outcomes (PROs) improved significantly, with post-operative pain VAS scores reduced by 28.4% and complication rates declining from 7.2% to 3.1% in the AI-guided cohort. Radiomics-driven analysis of 11,500 CBCT volumes and 34,000 periapical radiographs formed the annotated dataset backbone. This study provides high-level evidence for the clinical utility, safety, and efficiency of AI integration in contemporary dental practice, while also identifying ethical and regulatory challenges for future deployment.

    Keywords: Artificial Intelligence, Dental Diagnostics, Oral Surgery, Convolutional Neural Networks, CBCT, Deep Learning, Radiomics, Implant Planning, Oral Cancer Screening, Periodontal Assessment.

    Received: 17/05/2026

    Accepted: 04/06/2026

    Published: 07/06/2026

    DOI: 10.37067/ralap.2026.aimlfms.spec.01

    Citation: Aryan, R., & Hiwarkar, T. (2026). Revolutionising dental care through artificial intelligence: Applications in oral surgery and diagnostics. Revista Latinoamericana de la Papa, 1 [Special Edition: Artificial Intelligence and Machine Learning for Modern Systems], 1–10. Available online at https://ojs.papaslatina.org/revolutionising-dental-care-through-artificial-intelligence-applications-in-oral-surgery-and-diagnostics/

    Author(s) Retains the Copyrights of This Article








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  • To Study the Insurance Claims Detection Using Machine Learning

    Technical Notes

    Authors:

    Rishabh Aryan, B.Tech (Computer Science and Engineering), MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.

    Amit Kumar Sahu, Assistant Professor, Department of Computer Science and Engineering, MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.

    Corresponding Author: Rishabh Aryan, B.Tech (Computer Science and Engineering), MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.

    Email ID: rishabharyan07052004@gmail.com

    Abstract:

    The proliferation of fraudulent insurance claims poses a significant challenge to the insurance industry, leading to substantial financial losses and eroding trust among policyholders. This study delves into the realm of Fraudulent Insurance Claims Detection Using Machine Learning, a dynamic approach aimed at harnessing the power of artificial intelligence to mitigate this pervasive issue.


    With the advent of advanced technologies and the availability of vast datasets, machine learning algorithms have emerged as a potent tool for automating the detection and prevention of fraudulent insurance claims. This research undertakes a comprehensive exploration of the subject, focusing on the
    development and evaluation of machine learning models designed to distinguish genuine claims from
    fraudulent ones.


    The study encompasses various facets of the fraudulent claims detection process, including data preprocessing, feature engineering, model selection, and performance evaluation. Machine learning algorithms such as Random Forest, Support Vector Machine, Neural Networks, and Decision Trees are
    scrutinized for their efficacy in identifying suspicious patterns and anomalies within insurance claims data. Natural Language Processing (NLP) techniques are also applied to textual information to extract valuable insights.


    Results obtained from extensive experimentation with real-world insurance datasets reveal promising outcomes. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) statistics are employed to gauge the model performance, including metrics such as True Positives (TP),
    True Negatives (TN), False Positives (FP), and False Negatives (FN).


    Additionally, this research investigates the integration of deep learning and generative adversarial networks (GANs) to enhance the accuracy and robustness of fraudulent claims detection. Practical aspects such as model deployment through Application Programming Interfaces (APIs) and user-friendly Graphical User Interfaces (GUIs) are also considered for real-world implementation.


    The findings from this study hold immense potential for the insurance industry, leading to improved fraud prevention and more efficient claims processing. Moreover, the research underscores the importance of proactive strategies in combating insurance fraud, offering a substantial return on investment (ROI) for insurers. By leveraging machine learning techniques and adhering to data-driven methodologies, the industry can better protect its financial stability while preserving the trust of policyholders.

    Keywords -Insurance Fraud Detection, Machine Learning Algorithms, Deep Learning, Natural Language Processing (NLP), Anomaly Detection, ROC-AUC Evaluation.

    Accepted: 04/11/2022

    Published: 30/11/2022

    Author(s) Retains the Copyrights of This Article








    To Study the Insurance Claims Detection Using Machine Learning






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