AI-Assisted Diagnosis of Bone Tuberculosis: A Design Science Research Approach

Bone Tuberculosis (TB) is a significant public health challenge requiring early and precise diagnosis for effective treatment. Traditional methods like radiography and biopsy are invasive and costly. Our study introduces a holistic AI-assisted orthopedic clinical diagnosis system developed through an Action Design Research approach. Unlike previous efforts focused solely on algorithmic design, our system is iteratively validated with real-world clinical data, ensuring both theoretical rigor and practical applicability. By fine-tuning AI algorithms to meet actual clinical needs, we bridge the gap between technological innovation and healthcare relevance. Our research offers innovative insights into the design and evaluation of AI-assisted systems, emphasizing the role of empirical data and diverse evaluation metrics. The study is expected to have broader implications for the adoption of AI in clinical settings, offering a more comprehensive and reliable solution for bone TB diagnosis

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Lili Yuan,1,* Lin An,1,* Yandong Zhu,1 Chongling Duan,1 Weixiang Kong,1 Pei Jiang,2 Qing-Qing Yu1 1Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China; 2Translational Pharmaceutical Laboratory, Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qing-Qing Yu, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, 272000, People’s Republic of China, Email [email protected] Pei Jiang, Translational Pharmaceutical Laboratory, Jining NO.1 People’s Hospital, Shandong First Medical University, Jining, 272000, People’s Republic of China, Email [email protected]: As a disease with high morbidity and high mortality, lung cancer has seriously harmed people’s health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.Keywords: machine learning, computed tomography, lung cancer, artificial intelligence, diagnosi