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Author: search.filters.author.Lochanachit, Sirasit

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    AI Chatbot for Post-Operative Oral Surgery Information and Support
    (2025-08-07)
    Warin, Kritsasith
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    Taetragoo, Unchalisa
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    Trachoo, Vorapat
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    Khanijoh, Chanon
    ;
    Saepong, Pechdanai
    This study presented the development of an AI chatbot specifically designed to address oral surgery-related inquiries. The chatbot, trained on a dataset of 240 questions, utilized machine learning algorithms to predict the corresponding operation for each question. The best model achieved accuracy of 0.906 in responding to questions. In conclusion, this AI chatbot has the potential to improve patient care by providing clear information to enhance post-operative outcomes.
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    Prediction of Medication-Related Osteonecrosis of the Jaw in Patients Receiving Antiresorptive Therapy Using Machine Learning Models
    (2025-03-01)
    Warin, Kritsasith
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    Lochanachit, Sirasit
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    Pavarangkoon, Praphan
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    Techapanurak, Engkarat
    ;
    Somyanonthanakul, Rachasak
    Background: Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication associated with the use of antiresorptive agents, impacting patient quality of life and treatment outcomes. Predictive modeling may aid in a better understanding of MRONJ development. Purpose: The study aimed to evaluate machine learning (ML)–based models for predicting MRONJ in patients receiving antiresorptive therapy. Study Design, Setting, Sample: This retrospective in silico study analyzed electronic medical records from Thammasat University Hospital, covering the period from January 2012 to December 2022. The sample included subjects receiving antiresorptive therapy, excluding those with a history of radiation therapy or metastatic jaw disease. Predictor Variables: The primary predictor variable was the predicted probability of MRONJ development from the ML models. Outcome Variables: The outcome variable was MRONJ status coded as present or absent based on chart review. Covariates: Covariates included demographic data, MRONJ occurrence, location and staging of MRONJ, comorbidities, diseases related to antiresorptive agents, types of antiresorptive agents, therapy duration, concurrent medications, blood calcium levels, and dental factors. Analyses: Model performance was assessed via accuracy, sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve. Additionally, univariate and multivariate Cox regression analyses were conducted to identify factors significantly associated with MRONJ development. P ≤ .05 was statistically significant. Results: The study analyzed data from 5,305 subjects with a mean age of 75 ± 11.1 years, predominantly female. MRONJ was observed in 81 cases (1.5%), with a median time to development of 33 months (interquartile range = 3). Among the 6 models tested, the best-performing model had an accuracy of 0.95 and an area under the receiver operating characteristic curve of 0.89-0.90. Significant predictors identified through Cox regression included metabolic syndrome (hazard ratio = 14.064, 95% confidence interval = 1.111-178.067, P = .041) and patients receiving intravenous pamidronate (hazard ratio = 5.932, 95% confidence interval = 1.755-20.051, P = .004), indicating their association with MRONJ development. Conclusions and Relevance: ML-based predictive and time-to-event models effectively predict MRONJ risk, aiding in the strategic prevention and management for patients undergoing antiresorptive therapy.
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    Evaluation and Optimization of LLM and RAG Components for a Post-Operative Oral Surgery Consultation Chatbot
    (2025-01-01)
    Lochanachit, Sirasit
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    Bunlaue, Patcharamon
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    Kaewmuneechoke, Chanapat
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    Wilairatanaporn, Nopasorn
    ;
    Trachoo, Vorapat
    The increasing demand for dental services highlights the need for efficient post-operative oral surgery consultations. Many patients experience anxiety due to limited knowledge of oral care and treatment. This study introduces a chatbot prototype integrating Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to provide accurate, context-aware responses. The research evaluates various LLMs, embedding models, and chunking techniques to enhance chatbot performance. The multilingual-e5-large embedding model excelled in retrieval tasks due to its multilingual training, instruction tuning, and contrastive pre-training, ensuring high retrieval precision. The Hybrid Chunking method was selected for its ability to segment text contextually, combining Markdown-based, token-based, and semantic segmentation for optimal chunk relevance. The Llama3.3 (70B) model was chosen for its superior fluency, relevance, and ability to handle complex dependencies. The results demonstrate that combining the multilingual-e5-large embedding model, Hybrid Chunking technique, and Llama3.3 (70B) model improves retrieval precision, response accuracy, and relevance, enhancing patient care and operational effectiveness of dental staffs.
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    Impact of Rhythm, Tempo, and Rest Variations on Pitch Detection in Deep Learning-Based Piano Transcription Models
    (2024-01-01)
    Pangwapee, Priyakorn
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    Mekkoktanphira, Juthakan
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    Dilokthanakul, Nat
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    Lochanachit, Sirasit
    ;
    Kanungsukkasem, Nont
    This paper investigates the impact of rhythm, tempo, and rest variations on pitch detection in deep learning-based models for piano transcription. We conducted a series of experiments using GRU and Transformer architectures, manipulating note lengths, rhythmic patterns, and rest intervals to assess their effect on pitch transcription accuracy. Our findings indicate that model performance is significantly influenced by these musical factors. The experiment with GRU shows notable sensitivity to rhythmic and rest changes. However, the Transformer model handles varied conditions more robustly. These findings help refine our approach to music transcription software, particularly in improving pitch recognition across varied rhythmic patterns, tempos and rests.