Prior knowledge-based precise diagnosis of blend sign from head computed tomography.

Introduction: Automated diagnosis of intracranial hemorrhage on head computed tomography (CT) plays a decisive role in clinical management. This paper presents a prior knowledge-based precise diagnosis of blend sign network from head CT scans. Method: We employ the object detection task as an auxiliary task in addition to the classification task, which could incorporate the hemorrhage location as prior knowledge into the detection framework. The auxiliary task could help the model pay more attention to the regions with hemorrhage, which is beneficial for distinguishing the blend sign. Furthermore, we propose a self-knowledge distillation strategy to deal with inaccuracy annotations. Results: In the experiment, we retrospectively collected 1749 anonymous non-contrast head CT scans from the First Affiliated Hospital of China Medical University. The dataset contains three categories: no intracranial hemorrhage (non-ICH), normal intracranial hemorrhage (normal ICH), and blend sign. The experimental results demonstrate that our method performs better than other methods. Discussion: Our method has the potential to assist less-experienced head CT interpreters, reduce radiologists' workload, and improve efficiency in natural clinical settings.

Predictive model of risk factors of High Flow Nasal Cannula using machine learning in COVID-19.

With the rapid increase in the number of COVID-19 patients in Japan, the number of patients receiving oxygen at home has also increased rapidly, and some of these patients have died. An efficient approach to identify high-risk patients with slowly progressing and rapidly worsening COVID-19, and to avoid missing the timing of therapeutic intervention will improve patient prognosis and prevent medical complications. Patients admitted to medical institutions in Japan from November 14, 2020 to April 11, 2021 and registered in the COVID-19 Registry Japan were included. Risk factors for patients with High Flow Nasal Cannula invasive respiratory management or higher were comprehensively explored using machine learning. Age-specific cohorts were created, and severity prediction was performed for the patient surge period. We were able to obtain a model that was able to predict severe disease with a sensitivity of 57% when the specificity was set at 90% for those aged 40-59 years, and with a specificity of 50% and 43% when the sensitivity was set at 90% for those aged 60-79 years and 80 years and older, respectively. We were able to identify lactate dehydrogenase level (LDH) as an important factor in predicting the severity of illness in all age groups. Using machine learning, we were able to identify risk factors with high accuracy, and predict the severity of the disease. We plan to develop a tool that will be useful in determining the indications for hospitalisation for patients undergoing home care and early hospitalisation.

Interim 2019/2020 Influenza Vaccine Effectiveness in Japan from October 2019 to January 2020.

Herein, we report the interim vaccine effectiveness (VE) of a quadrivalent inactivated influenza vaccine, during the 2019/2020 influenza season, in Japan. We conducted a retrospective observational cohort study of 381 patients aged ≥15 years, who were enrolled with influenza like illnesses and examined via the rapid influenza diagnostic test, at the Ambulatory Care unit of the National Center for Global Health and Medicine in Tokyo, Japan, from the beginning of October 2019 to the end of January 2020. VE was estimated using a test-negative design. VE was calculated as (1 - odds ratio) × 100%, comparing influenza A test positivity between vaccinated and unvaccinated patients. Of the 381 patients initially screened for inclusion, 314 were enrolled in the study. Of these, 105 were vaccinated, 98 were diagnosed with influenza A, and 5 were diagnosed with influenza B. Overall VE against influenza A was 27.6% (95% confidence interval [CI], ‒21.1 to +57.4), and in patients aged ≥65 years, it was 47.3% (95% CI, ‒76.0 to +86.0). This indicates that the influenza vaccination offered continued protection during the 2019/2020 influenza season, but a detailed analysis of more cases with a careful consideration of methodology is necessary to estimate VE more precisely.

Two Different Minimally Invasive Surgery Puncture Points with Thrombolysis in a Patient with Bilateral Basal Ganglia Hemorrhages.

Bilateral basal ganglia hemorrhages are extremely rare and have very poor prognosis. We describe the case of a 52-year-old woman with a history of hypertension who experienced bilateral basal ganglia hemorrhages. We performed bilateral hematoma aspiration by minimally invasive surgery via frontal and temporal puncture points. We discuss the surgical procedure and review relevant literature pertaining to the underlying causes and complications of similar cases.