"KAIZEN" method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals.

Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primarily owing to methodological defects and the lack of implementation considerations for inference. In this study, all development processes of the deep-learning models are performed based on strict criteria of the "KAIZEN checklist", which is proposed based on previous AI development guidelines to overcome the deficiencies mentioned above. We develop and evaluate two binary-classification deep-learning models to triage COVID-19: a slice model examining a Computed Tomography (CT) slice to find COVID-19 lesions; a series model examining a series of CT images to find an infected patient. We collected 2,400,200 CT slices from twelve emergency centers in Japan. Area Under Curve (AUC) and accuracy were calculated for classification performance. The inference time of the system that includes these two models were measured. For validation data, the slice and series models recognized COVID-19 with AUCs and accuracies of 0.989 and 0.982, 95.9% and 93.0% respectively. For test data, the models' AUCs and accuracies were 0.958 and 0.953, 90.0% and 91.4% respectively. The average inference time per case was 2.83 s. Our deep-learning system realizes accuracy and inference speed high enough for practical use. The systems have already been implemented in four hospitals and eight are under progression. We released an application software and implementation code for free in a highly usable state to allow its use in Japan and globally.

Sedation Therapy in Intensive Care Units: Harnessing the Power of Antioxidants to Combat Oxidative Stress.

In critically ill patients requiring intensive care, increased oxidative stress plays an important role in pathogenesis. Sedatives are widely used for sedation in many of these patients. Some sedatives are known antioxidants. However, no studies have evaluated the direct scavenging activity of various sedative agents on different free radicals. This study aimed to determine whether common sedatives (propofol, thiopental, and dexmedetomidine (DEX)) have direct free radical scavenging activity against various free radicals using in vitro electron spin resonance. Superoxide, hydroxyl radical, singlet oxygen, and nitric oxide (NO) direct scavenging activities were measured. All sedatives scavenged different types of free radicals. DEX, a new sedative, also scavenged hydroxyl radicals. Thiopental scavenged all types of free radicals, including NO, whereas propofol did not scavenge superoxide radicals. In this retrospective analysis, we observed changes in oxidative antioxidant markers following the administration of thiopental in patients with severe head trauma. We identified the direct radical-scavenging activity of various sedatives used in clinical settings. Furthermore, we reported a representative case of traumatic brain injury wherein thiopental administration dramatically affected oxidative-stress-related biomarkers. This study suggests that, in the future, sedatives containing thiopental may be redeveloped as an antioxidant therapy through further clinical research.