RESUMO
Artificial intelligence can potentially provide a substantial role in streamlining chest computed tomography (CT) diagnosis of COVID-19 patients. However, several critical hurdles have impeded the development of robust AI model, which include deficiency, isolation, and heterogeneity of CT data generated from diverse institutions. These bring about lack of generalization of AI model and therefore prevent it from applications in clinical practices. To overcome this, we proposed a federated learning-based Unified CT-COVID AI Diagnostic Initiative (UCADI, http://www.ai-ct-covid.team/), a decentralized architecture where the AI model is distributed to and executed at each host institution with the data sources or client ends for training and inferencing without sharing individual patient data. Specifically, we firstly developed an initial AI CT model based on data collected from three Tongji hospitals in Wuhan. After model evaluation, we found that the initial model can identify COVID from Tongji CT test data at near radiologist-level (97.5% sensitivity) but performed worse when it was tested on COVID cases from Wuhan Union Hospital (72% sensitivity), indicating a lack of model generalization. Next, we used the publicly available UCADI framework to build a federated model which integrated COVID CT cases from the Tongji hospitals and Wuhan Union hospital (WU) without transferring the WU data. The federated model not only performed similarly on Tongji test data but improved the detection sensitivity (98%) on WU test cases. The UCADI framework will allow participants worldwide to use and contribute to the model, to deliver a real-world, globally built and validated clinic CT-COVID AI tool. This effort directly supports the United Nations Sustainable Development Goals number 3, Good Health and Well-Being, and allows sharing and transferring of knowledge to fight this devastating disease around the world.
RESUMO
Background and purposeThe worldwide pandemic of coronavirus disease 2019 (COVID-19) greatly challenges public medical systems. With limited medical resources, the treatment priority is determined by the severity of patients. However, many mild outpatients quickly deteriorate into severe/critical stage. It is crucial to early identify them and give timely treatment for optimizing treatment strategy and reducing mortality. This study aims to establish an AI model to predict mild patients with potential malignant progression. MethodsA total of 133 consecutively mild COVID-19 patients at admission who was hospitalized in Wuhan Pulmonary Hospital from January 3 to February 13, 2020, were selected in this retrospective IRB-approved study. All mild patients were categorized into groups with or without malignant progression. The clinical and laboratory data at admission, the first CT, and the follow-up CT at the severe/critical stage of the two groups were compared. Both multivariate logistic regression and deep learning-based methods were used to build the prediction models, with their area under ROC curves (AUC) compared. ResultsMultivariate logistic regression depicted 6 risk factors for malignant progression: age >55years (OR 5.334, 95%CI 1.8-15.803), comorbid with hypertension (OR 5.093, 95%CI 1.236-20.986), a decrease of albumin (OR 4.01, 95%CI 1.216-13.223), a decrease of lymphocyte (OR 3.459, 95%CI 1.067-11.209), the progressive consolidation from CT1 to CTsevere (OR 1.235, 95%CI 1.018-1.498), and elevated HCRP (OR 1.015, 95%CI 1.002-1.029); and one protective factor: the presence of fibrosis at CT1 (OR 0.656, 95%CI 0.473-0.91). By combining the clinical data and the temporal information of the CT data, our deep learning-based models achieved the best AUC of 0.954, which outperformed logistic regression (AUC: 0.893), ConclusionsOur deep learning-based methods can identify the mild patients who are easy to deteriorate into severe/critical cases efficiently and accurately, which undoubtedly helps to optimize the treatment strategy, reduce mortality, and relieve the medical pressure.
RESUMO
Objective To explore the diagnosis and treatment strategy of mammary duct ectasia.Methods The clinical data of 59 cases with mammary duct ectasia from January 2006 to December 2013 were analyzed retrospectively.Results The main clinical manifestations of mammary duct ectasia were mammary inflammatory mass in 46 cases,nipple discharge in 21 cases,nipple retraction in 18 cases,mammary abscess and mammary fistula in 8 cases.Definite diagnosis of mammary duct ectasia depended on pathology.All the patients were treated by operation,followed up for 3 months to 6 years,and none of them had recurrence.Conclusions Operation is the main method of curing mammary duct ectasia.To select proper operation time and method according to disease type,lesion size,location and scope.Thorough resection,repeat rinsing,wound clearance and immediate breast shape can not only cure disease,but also reserve breast configuration as possible.Therapeutic effect is satisfactory.
RESUMO
Affine motion is common during PROPELLER magnetic resonance imaging (MRI) in abdomen or other soft tissues. The current algorithm, up till now, for affine motion compensation is based on frequency domain, which compensates the motion in k space and then reconstruct the final image based on gridding method. But aliasing and some tiny artifacts may exist. This paper proposed a new algorithm for affine motion compensation based on image domain. Firstly, exact affine motion information was obtained through the image registration, secondly k space coordinate was corrected for compensating the k space strips sampling density, then the images obtained from inverse FFT was compensated using motion information, finally the final results were composited after rotation. The experimental results showed that the proposed method could more effectively suppress the motion artifacts compared to the current algorithm.
