Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study

Chilamkurthy, Sasank; Ghosh, Rohit; Tanamala, Swetha; Biviji, Mustafa; Campeau, Norbert G.; Venugopal, Vasanthakumar; Mahajan, Vidur; Rao, Pooja; Warier, Prashant

doi:10.1016/s0140-6736(18)31645-3

Cited by 769 publications

(584 citation statements)

References 48 publications

(54 reference statements)

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“…On the task of predicting the risk of lung cancer [13], the deep learning model was trained on 42290 CT cases from 14851 patients and obtained 0.944 ROC AUC. On the task of critical findings from head CT [23], the deep learning model was trained on 310055 head CT scans and obtained ROC AUC of 0.920. In our study, only 499 scans were used for training, but the obtained ROC AUC was 0.959.…”

Section: Patient and Public Involvementmentioning

confidence: 99%

Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label

Zheng

Deng

et al. 2020

Preprint

539

450

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Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 detection on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning-based software system was developed using 3D CT volumes to detect COVID-19. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious. 499 CT volumes collected from Dec. 13, 2019, to Jan. 23, 2020, were used for training and 131 CT volumes collected from Jan 24, 2020, to Feb 6, 2020, were used for testing. The deep learning algorithm obtained 0.959 ROC AUC and 0.976 PR AUC. There was an operating point with 0.907 sensitivity and 0.911 specificity in the ROC curve. When using a probability threshold of 0.5 to classify COVID-positive and COVID-negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient's CT volume using a dedicated GPU. Our weaklysupervised deep learning model can accurately predict the COVID-19 infectious probability in chest CT volumes without the need for annotating the lesions for training. The easily-trained and highperformance deep learning algorithm provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-CoV-2. The developed deep learning software is available at https://github.com/sydney0zq/covid-19-detection.

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Section: Patient and Public Involvementmentioning

confidence: 99%

Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label

Zheng

Deng

et al. 2020

Preprint

539

450

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“…Much interesting work has been performed for the automated ICH diagnosis. The majority of this work has focused either on a two-class detection problem where the method detects the presence of an ICH [6][7][8][9][10][11][12][13][14][15][16][16][17][18][19] or as a multi-class classification problem, where the goal is to detect the ICH sub-types [6,8,11,15,[17][18][19]. Some researchers have extended the scope and performed the ICH segmentation to identify the region of ICH [7,11,15,17,[19][20][21][22][23][24][25][26].…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, there is only one publicly available dataset called CQ500 for the detection of ICH sub-types [6] that consists of 491 head CT scans. There is no publicly available dataset for the ICH segmentation.…”

Section: Introductionmentioning

confidence: 99%

Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

Hssayeni

Croock

Salman

et al. 2020

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Traumatic brain injuries could cause intracranial hemorrhage (ICH). ICH could lead to disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure. The current clinical protocol to diagnose ICH is examining Computerized Tomography (CT) scans by radiologists to detect ICH and localize its regions. However, this process relies heavily on the availability of an experienced radiologist. In this paper, we designed a study protocol to collect a dataset of 82 CT scans of subjects with traumatic brain injury. Later, the ICH regions were manually delineated in each slice by a consensus decision of two radiologists. Recently, fully convolutional networks (FCN) have shown to be successful in medical image segmentation. We developed a deep FCN, called U-Net, to segment the ICH regions from the CT scans in a fully automated manner. The method achieved a Dice coefficient of 0.31 for the ICH segmentation based on 5-fold cross-validation. The dataset is publicly available online at PhysioNet repository for future analysis and comparison. Dataset: https://physionet.org/content/ct-ich/1.2.0/,

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“…Specifically, we employed a deep learning-based model to effectively mine the complementary information in static clinical data and serial quantitative chest CT sequence. Since deep learning-based methods had been widely adopted and had achieved great performance in cancer outcome prediction 14 , head CT scans detection 15 and antibiotic discovery 16 . Moreover, compared with the traditional multi-stage methods, the deep learning-based model could significantly improve the efficiency of patient stratification (Figure 1), which is very important when dealing with tremendous patients.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for predicting COVID-19 malignant progression

Fang

Bai

Chen

et al. 2020

Preprint

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Background and purpose: The worldwide pandemic of coronavirus disease 2019 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.Methods: A 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.Results: Multivariate 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), Conclusions: Our 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.

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Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study

Cited by 769 publications

References 48 publications

Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label

Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label

Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

Deep learning for predicting COVID-19 malignant progression

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