Puengpipattrakul, Paisal

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Preferred name
Puengpipattrakul, Paisal
Alternative Name
Puengpipattrakul, P.
Main Affiliation
Email
paisal.pu@kmitl.ac.th

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    Artificial intelligence assistance in radiographic detection and classification of knee osteoarthritis and its severity: A cross-sectional diagnostic study
    (2022-01-01)
    Pongsakonpruttikul, N.
    ;
    Angthong, Chayanin
    ;
    Kittichai, V.
    ;
    Chuwongin, S.
    ;
    Puengpipattrakul, P.
    OBJECTIVE: Radiographic interpretation suffers from an ever-increasing workload in orthopedic and radiology departments. The present study applied and assessed the performance of a convolutional neural network designed to assist orthopedists and radiologists in the detection and classification of knee osteoarthritis from early to severe degrees in accordance with the Kellgren-Lawrence (KL) classification system. MATERIALS AND METHODS: In total, 1650 knee joint radiographs (anteroposterior view) were collected from the Osteoarthritis Initiative public resource. Two models were developed: one distinguished normal (KL 0-I) from osteoarthritic knees (KL II-IV), and the other classified the severity as normal (KL 0-I), non-severe (KL II), or severe (KL III-IV). The regions of interest were labeled under the supervision of experts. Our artificial intelligence (AI) models were trained using the You Only Look Once version 3 (YOLOv3) detection algorithm. RESULTS: Our first AI model using YOLOv3 tiny could detect and classify normal and osteoarthritic knees on plain knee joint radiographs with 85% accuracy and 81% mean average precision. The second AI model for classifying severity achieved a total accuracy of 86.7% and mean average precision of 70.6%. CONCLUSIONS: Our proposed deep learning models provided high accuracy and satisfactory precision for the detection and classification of early to severe knee osteoarthritis on anteroposterior radiographs. These models may be used as diagnostic aids by interpreting knee radiographs and guiding the treatment options via each osteoarthritic stage for related physicians and specialists.
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    Prevalence of tuberculosis (TB), including multi-drug-resistant and extensively-drug-resistant TB, and association with occupation in adults at Sirindhorn Hospital, Bangkok
    (2022-03-01)
    Mphande-Nyasulu, Fingani Annie
    ;
    Puengpipattrakul, Paisal
    ;
    Praipruksaphan, Mathuwadee
    ;
    Keeree, Arada
    ;
    Ruanngean, Karakade
    Background: Tuberculosis (TB) affects both child and adult populations worldwide. Objectives: This retrospective study was conducted to survey the prevalence of TB and its association with patient occupation in an adult population diagnosed with TB at Sirindhorn Hospital in 2018. Methods: Data were extracted from the medical records of 186 patients with TB, and prevalence and odds ratios were calculated. Results: Pulmonary (83.3%) and extrapulmonary TB (17.7%) were observed among the cases. Overall, 70.4% of cases were male and 29.6% were female. Mono-drug-resistant TB, multi-drug-resistant TB and extensively-drug-resistant TB were observed in 2.72%, 4.1% and 0.68% of cases, respectively. Although not statistically significant, individuals with comorbidities had a 2.16-fold [95% confidence interval (CI) 0.33–13.98] higher risk of TB compared with those without comorbidities. Unemployed patients with TB were 4-fold (95% CI 0.82–19.42) more likely to have hypertension than employed patients or traders. The risk of TB among patients with human immunodeficiency virus (HIV) infection was 2.22-fold (95% CI 0.93–5.31) higher among females compared with males, and relapsed patients had a 0.92-fold (95% CI 0.19–4.47) lower risk of HIV infection as a comorbidity compared with new TB cases. Conclusion: Patient occupation could play a role in the prevalence of TB among communities. The highest prevalence of TB was observed among unemployed subjects, and unemployed patients with TB were more likely to have hypertension as a comorbidity. Mapping the zones/areas of residence for patients with TB could assist in identifying TB hot spots, and could improve understanding of the drivers of the high TB burden and associated socio-economic factors. More studies are required to further understand the drivers that are leading to the high TB burden and the risks posed by occupations.