ABSTRACT
Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.
ABSTRACT
Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles. The application of deep learning technology in medicine is sometimes restricted by ethical or legal issues, including patient privacy and confidentiality, data ownership, and limitations in patient agreement. In this paper, we present an open platform, MI2RLNet, for sharing source code and various pre-trained weights for models to use in downstream tasks, including education, application, and transfer learning, to encourage deep learning research in radiology. In addition, we describe how to use this open platform in the GitHub environment. Our source code and models may contribute to further deep learning research in radiology, which may facilitate applications in medicine and healthcare, especially in medical imaging, in the near future. All code is available at https://github.com/mi2rl/MI2RLNet.
ABSTRACT
BACKGROUND: Cigarette smoking is a risk factor for cardiovascular disease (CVD) and has both beneficial and harmful effects in CVD. We hypothesized that weight gain following smoking cessation does not attenuate the CVD mortality of smoking cessation in the general Korean population. METHODS: Study subjects comprised 2.2% randomly selected patients from the Korean National Health Insurance Corporation, between 2002 and 2013. We identified 61,055 subjects who were classified as current smokers in 2003–2004. After excluding 21,956 subjects for missing data, we studied 30,004 subjects. We divided the 9,095 ex-smokers into two groups: those who gained over 2 kg (2,714), and those who did not gain over 2 kg (6,381, including weight loss), after smoking cessation. Cox proportional hazards regression models were used to estimate the association between weight gain following smoking cessation and CVD mortality. RESULTS: In the primary analysis, the hazard ratios of all-cause deaths and CVD deaths were assessed in the three groups. The CVD risk factors and Charlson comorbidity index adjusted hazard ratios (aHRs) for CVD deaths were 0.80 (95% confidence interval [CI], 0.37 to 1.75) for ex-smokers with weight gain and 0.80 (95% CI, 0.50 to 1.27) for ex-smokers with no weight gain, compared to one for sustained smokers. The associations were stronger for events other than mortality. The aHRs for CVD events were 0.69 (95% CI, 0.54 to 0.88) and 0.81 (95% CI, 0.70 to 0.94) for the ex-smokers with and without weight gain, respectively. CONCLUSION: Although smoking cessation leads to weight gain, it does not increase the risk of CVD death.