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Artificial intelligence assistance in deciding management strategies for polytrauma and trauma patients
(2024-01-01)
Angthong, Chayanin
;
Rungrattanawilai, Naruebade
;
Pundee, Chaiyapruk
Introduction: Artificial intelligence (AI) is an emerging technology with vast potential for use in several fields of medicine. However, little is known about the application of AI in treatment decisions for patients with polytrauma. In this systematic review, we investigated the benefits and performance of AI in predicting the management of patients with polytrauma and trauma. Methods: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were extracted from the PubMed and Google Scholar databases from their inception until November 2022, using the search terms “Artificial intelligence” AND “polytrauma” AND “decision”. Seventeen articles were identified and screened for eligibility. Animal studies, review articles, systematic reviews, meta-analyses, and studies that did not involve polytrauma or severe trauma management decisions were excluded. Eight studies were eligible for final review. Results: Eight studies focusing on patients with trauma, including two on military trauma, were included. The AI applications were mainly implemented for predictions and/or decisions on shock, bleeding, and blood transfusion. Few studies predicted death/survival. The identification of trauma patients using AI was proposed in a previous study. The overall performance of AI was good (six studies), excellent (one study), and acceptable (one study). Discussion: AI demonstrated satisfactory performance in decision-making and management prediction in patients with polytrauma/severe trauma, especially in situations of shock/bleeding. Importance: The present study serves as a basis for further research to develop practical AI applications for the management of patients with trauma.
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Detection of knee osteoarthritis using artificial intelligence
(2023-11-06)
Thongpat, Pongphak
;
Pongsakonpruttikul, Napat
;
Angthong, Chayanin
Knee osteoarthritis (KOA) is a common degenerative joint disease that results in disability due to joint dysfunction and pain. Almost one-fifth of early KOA cases are missed during the routine practice resulting in the progression of the disease. This narrative review aimed to explore and analyze various literatures that proposed Convoluted Neural Network (CNN) model in detecting KOA and its severity based on Kellgren Lawrence grading classification. At first, 221 publications were retrieved using the search term "artificial intelligence" and Knee osteoarthritis". Only studies that used CNN and radiographic images were included in this study in which only 14 studies fitted our inclusion criteria. Each paper was thoroughly investigated for the input data and CNN model adopted as well as the performance and limitation of that study. Lastly, the conclusion was made and discussed using these results. Object detection and Classification models were among the most popular techniques adopted. Our results showed that object detection models were overall superior regarding the accuracy in the detection of KOA and its severity. The application of CNN for the detection of KOA from radiographic images has shown great promise where each technique has its own advantage. In the foreseeable future, the combination of object detection and classification detection may provide excellent potential as a merit tool to help orthopedists and related physicians for the proper diagnosis and treatment of KOA.
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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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