Dataset Information

Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.

ABSTRACT: Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.

SUBMITTER: Zhang K

PROVIDER: S-EPMC7196900 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.

Zhang Kang K Liu Xiaohong X Shen Jun J Li Zhihuan Z Sang Ye Y Wu Xingwang X Zha Yunfei Y Liang Wenhua W Wang Chengdi C Wang Ke K Ye Linsen L Gao Ming M Zhou Zhongguo Z Li Liang L Wang Jin J Yang Zehong Z Cai Huimin H Xu Jie J Yang Lei L Cai Wenjia W Xu Wenqin W Wu Shaoxu S Zhang Wei W Jiang Shanping S Zheng Lianghong L Zhang Xuan X Wang Li L Lu Liu L Li Jiaming J Yin Haiping H Wang Winston W Li Oulan O Zhang Charlotte C Liang Liang L Wu Tao T Deng Ruiyun R Wei Kang K Zhou Yong Y Chen Ting T Lau Johnson Yiu-Nam JY Fok Manson M He Jianxing J Lin Tianxin T Li Weimin W Wang Guangyu G

Cell 20200504 6

Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicia ...[more]

PMID: 32416069

Similar Datasets

Project description:Objective [18F]Fluorocholine positron emission tomography/computed tomography (PET/CT) is used frequently in addition to [99mTc]Tc-Sestamibi scintigraphy and ultrasonography for the location of hyperfunctioning parathyroid glands. The aim of this study is to evaluate the performance of quantitative criteria in [18F]fluorocholine PET/CT for localization of hyperfunctioning parathyroid glands. The secondary objective is to highlight a correlation between the detection rate of [18F]fluorocholine PET/CT and serum parathyroid hormone (PTH) level. Materials and methods In two academic centers, we retrospectively included patients with biological hyperparathyroidism (HPT) and who had [18F]fluorocholine PET/CT. After a visual analysis, to measure the overall performance of [18F]fluorocholine PET/CT, a blind reading was carried out with standardized measurements of maximum standardized uptake value (SUVmax), liver ratio, thyroid ratio, and size ratio. We analyzed the quantitative criteria of [18F]fluorocholine PET/CT compared to the histological results, in particular to identify differences between adenomas and hyperplasias. We compared the performance of each quantitative criterion to the overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of [18F]fluorocholine PET/CT. The detection rate of hyperfunctioning parathyroid glands was calculated in subgroups of serum PTH level. Results The quantitative criteria in [18F]fluorocholine PET/CT were measured for 120 patients (135 lesions). The areas under the receiver operating characteristic (ROC) curve representing SUVmax and liver ratio were significantly increased. The optimal cut-off values represented by the maximum Youden index was >4.12 for SUVmax and >27.4 for liver ratio. Beyond certain threshold values of SUVmax (>4.12) or liver ratio (>38.1), all the lesions were histologically proven adenomas. SUVmax and liver ratio were significantly higher for adenomas than for hyperplasias and differential diagnosis (p = 0.0085 and p = 0.0002). The positivity of [18F]fluorocholine PET/CT was correlated with PTH level. Detection rates were 55.56, 75.56, and 87.5%, respectively, for serum PTH < 70, 70 to 120, and >120 ng/ml. Conclusion Semi-quantitative measurements (SUVmax and liver ratio) should be considered as additional tools in interpretation of [18F]fluorocholine PET/CT. These quantitative parameters have lower overall performance but higher specificity than overall visual analysis in identifying an adenoma. Above certain threshold values, all lesions are adenomas. [18F]fluorocholine PET/CT confirms excellent performance for the detection of hyperfunctional parathyroids. For serum PTH levels < 70 ng/ml, the detection rate of [18F]fluorocholine PET/CT is strongly decreased.

Project description:Background Despite the clinical importance of assessing the efficiency and accuracy of fluency in terms of content words production during connected speech, assessments based on discourse tasks are very time-consuming and thus not clinically feasible. Aims (1) Examine the relationship between single-word naming and word retrieval during discourse production. (2) Investigate the relationship between word retrieval and content word fluency derived from a simple versus naturalistic discourse tasks. (3) Develop and validate an efficient and accurate index of content word fluency that is clinically viable. Methods Two discourse tasks (simple picture description and naturalistic storytelling narrative) were collected from 46 participants with post-stroke aphasia, and 20 age/education matched neuro-typical controls. Each discourse sample was fully transcribed and quantitative analysis was applied to each sample to measure word retrieval and content word fluency. Three single-word naming tasks were also administered to each participant with aphasia. Results Correlational analyses between single-word naming and word retrieval in connected speech revealed weak/moderate relationships. Conversely, strong correlations were found between measures derived from simple picture description against naturalistic storytelling discourse tasks. Moreover, we derived a novel, transcription-less index of content word fluency from the discourse samples of an independent group (neuro-typical controls), and then we validated this index across two discourse tasks in the tested group (persons with aphasia). Correlation and regression analyses revealed extremely strong relationships between participants’ (neuro-typical controls and persons with aphasia) scores on the novel index and measures of content word fluency derived from the formal transcription and quantitative analyses of discourse samples, indicating high accuracy and validity of the new index. Conclusions Simple picture description rather than picture naming provides a better estimate of word retrieval in naturalistic connected speech. The novel developed index is transcription-less and can be implemented online to provide an accurate and efficient measure of content word fluency. Thus, it is viable during clinical practice for assessment purposes, and possibly as an outcome measure to monitor therapy effectiveness, which can also be used in randomised clinical trials.

Project description:PurposeTo examine the use of multiple fast-helical free breathing computed tomography (FHFBCT) scans for ventilation measurement.MethodsTen patients were scanned 25 times in alternating directions using a FHFBCT protocol. Simultaneously, an abdominal pneumatic bellows was used as a real-time breathing surrogate. Regions-of-interest (ROIs) were selected from the upper right lungs of each patient for analysis. The ROIs were first registered using a published registration technique (pTV). A subsequent follow-up registration employed an objective function with two terms, a ventilation-adjusted Hounsfield Unit difference and a conservation-of-mass term labeled ΔΓ that denoted the difference between the deformation Jacobian and the tissue density ratio. The ventilations were calculated voxel-by-voxel as the slope of a first-order fit of the Jacobian as a function of the breathing amplitude.ResultsThe ventilations of the 10 patients showed different patterns and magnitudes. The average ventilation calculated from the deformation vector fields (DVFs) of the pTV and secondary registration was nearly identical, but the standard deviation of the voxel-to-voxel differences was approximately 0.1. The mean of the 90th percentile values of ΔΓ was reduced from 0.153 to 0.079 between the pTV and secondary registration, implying first that the secondary registration improved the conservation-of-mass criterion by almost 50% and that on average the correspondence between the Jacobian and density ratios as demonstrated by ΔΓ was less than 0.1. This improvement occurred in spite of the average of the 90th percentile changes in the DVF magnitudes being only 0.58 mm.ConclusionsThis work introduces the use of multiple free-breathing CT scans for free-breathing ventilation measurements. The approach has some benefits over the traditional use of 4-dimensional CT (4DCT) or breath-hold scans. The benefit over 4DCT is that FHFBCT does not have sorting artifacts. The benefits over breath-hold scans include the relatively small motion induced by quiet respiration versus deep-inspiration breath hold and the potential for characterizing dynamic breathing processes that disappear during breath hold.

Dataset Information

Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.

Publications

Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets