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Qure.ai Recognized Among TIME’s 100 Most Influential Companies of 2025

Qure.ai partners with Microsoft to increase access to its lung cancer detection and management suite in the U.S.

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Baptist Christian Hospital (BCH) Tezpur, a 130-bed charitable institution under the Emmanuel Health Association, operates without an in-house neurologist and relies on tele-radiology for NCCT scan analysis, often resulting in hours-long delays. The nearest comprehensive stroke center is 180 kms away in Guwahati. In collaboration with BCH,&nbsp;Qure.ai&nbsp;conducted a study exploring AI’s role in enhancing stroke diagnosis and treatment, particularly in remote or resource-limited settings.

Delayed Treatment:&nbsp;Before AI adoption, BCH faced significant delays in stroke diagnosis and interventions critical for patient survival and recovery.

Resource Constraints:&nbsp;Limited resources and the absence of specialist doctors often led to delayed decision-making and treatment.

<a href="https://www.qure.ai">Qure.ai’s</a> AI-powered solution, qER, transformed stroke care at BCH by:

Faster stroke diagnosis:&nbsp;The AI quickly and accurately analyzed head CT scans, effectively identifying strokes and ruling out hemorrhages with high precision.

Empowering Non-Specialists:&nbsp;AI-powered stroke solution enabled non-specialist doctors to make faster, more confident treatment decisions.

187% Increase in Early Interventions:&nbsp;AI implementation led to a 187% increase in the proportion of patients treated within the Golden Hour, significantly improving survival rates.

27% Reduction in Time to Treatment:&nbsp;The AI reduced the time to intervention by 27%, enabling quicker treatment.

Improved Diagnostic Accuracy:&nbsp;qER achieved 97% accuracy in ruling out bleeds, expediting treatment decisions.

The integration of AI at BCH has revolutionized stroke care, leading to significantly better patient outcomes. This success story demonstrates AI’s potential to bridge healthcare gaps, especially in resource-constrained environments. BCH’s experience stands as a model for other institutions aiming to enhance stroke care through innovative technology.

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qER publication PR - website thumbnail.webp

Enhancing Stroke Care with AI at Baptist Christian Hospital

A stroke occurs when an artery in the brain ruptures and bleeds or when the blood supply to the brain is cut off. Blood and oxygen cannot reach the brain’s tissues due to the rupture or obstruction resulting in tissue death. The Middle cerebral artery (MCA) is the largest cerebral artery and the most commonly damaged vessel in stroke. The quick onset of a focused neurological deficit caused by interruption of blood flow in the territory supplied by the MCA is known as an MCA stroke. Alberta stroke&nbsp;programme&nbsp;early CT score (ASPECTS) is used to estimate the extent of early ischemic changes in patients with MCA stroke. This study proposes a deep learning-based method to score the CT scan for ASPECTS. Our work has three highlights. First, we propose a novel method for medical image segmentation for stroke detection. Second, we show the effectiveness of AI solution for fully-automated ASPECT scoring with reduced diagnosis time for a given non-contrast CT (NCCT) Scan. Our algorithms show a dice similarity coefficient of 0.64 for the MCA anatomy segmentation and 0.72 for the infarcts segmentation. Lastly, we show that our model’s performance is&nbsp;inline&nbsp;with inter-reader variability between radiologists.&nbsp;

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Deep-ASPECTS A Segmentation-Assisted Model for Stroke Severity Measurement.webp

Deep-ASPECTS: A Segmentation-Assisted Model for Stroke Severity Measurement

Currently, no large dataset-derived standard has been established for normal or pathologic human cerebral ventricular and cranial vault volumes. Automated volumetric measurements could be used to assist in diagnosis and follow-up of hydrocephalus or craniofacial syndromes. In this work we use deep learning algorithms to measure ventricular and cranial vault volumes in a large dataset of head computed tomography (CT) scans.

A cross-sectional dataset comprising 13,851 CT scans was utilized to deploy U-net deep learning networks to segment and quantify lateral cerebral ventricular and cranial vault volumes in relation to age and sex. The models were validated against manual segmentations. Corresponding radiological reports were annotated using a rule-based natural language processing (NLP) framework to identify normal scans, cerebral atrophy, or hydrocephalus.

U-net models had high fidelity to manual segmentations for lateral ventricular and cranial vault volume measurements (DICE 0.878 and 0.983, respectively). The NLP identified 6,239 (44.7%) normal radiological reports, 1,827 (13.1%) with cerebral atrophy and 1,185 (8.5%) with hydrocephalus. Age- and sex-based reference tables with medians, 25th and 75th percentiles for scans classified as normal, atrophy and hydrocephalus were constructed. The median lateral ventricular volume in normal scans was significantly smaller compared to hydrocephalus (15.7mL vs 82.0mL, P&lt;0.001).

This is the first study to measure lateral ventricular and cranial vault volumes in a large dataset, made possible with artificial intelligence. We provide a robust method to establish normal values for these volumes and a tool to report these on CT scans when evaluating for hydrocephalus.

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Automated Lateral Ventricular and Cranial Vault Volume Measurements in 13,851 Subjects Utilizing Deep Learning Algorithms.webp

Automated Lateral Ventricular and Cranial Vault Volume Measurements in 13,851 Subjects Utilizing Deep Learning Algorithms

Clinical history plays a vital role in a physician's or radiologist's diagnosis. However, when training AI models, clinical history or presence of an abnormality which correlates to the target abnormality were not generally considered. In this study, we use scalp hematoma as an additional clinical context in training the models and study the accuracy (AUC and average precision) of a fracture detection AI model before and after adding this clinical context.&nbsp;

Using 141,105 studies, we trained a convolutional neural network (CNN) to detect cranial fractures on non-contrast head CT scans. Scalp hematoma is considered a good indicator by physicians for diagnosing fractures. We confirmed this by automated natural language processing (NLP) analysis of large number of reports. Therefore, scalp hematoma is a good candidate for improving AI algorithms for detecting fractures. A logistic regression model was trained to detect a cranial fracture, using the presence of a scalp hematoma and the output probability of the CNN as inputs. The original CNN by itself (Model 1) and the combined CNN-logistic regression algorithm (Model 2) were tested using an independent set containing 18200 scans. We used area under the ROC curve (AUC) and average precision (AP), a probability based metric that is inversely proportional to false positive rate, as evaluation metrics.&nbsp;

Analysis of 141,105 reports confirmed that scalp hematoma was present in 49.8% of scans with fractures and conversely fractures were present in 29.8% of scans with scalp hematoma. The CNN with images as sole inputs reached an AUC and AP of 0.9599 and 0.7952 respectively. Adding scalp hematoma as a feature increased AUC to 0.9666. AP however, increased significantly to 0.8190.&nbsp;

Using a simple probabilistic algorithm to add clinical context to a CNN resulted in a significant improvement in AP. As AUC is saturated, there is no significant difference in AUC. Results show significant decrease in false positive rate without impacting sensitivity.&nbsp;

Like radiologists, deep learning models can be more accurate when they incorporate clinical context in addition to image analysis.&nbsp;

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Clinical Context Improves the Performance of AI Models for Cranial Fracture Detection.webp

Clinical Context Improves the Performance of AI Models for Cranial Fracture Detection

True volumetric measurement of the cerebral ventricles could potentially improve the diagnosis and follow-up of hydrocephalus. However, manual image segmentation is an impractically laborious process to be applied routinely. In this work, we utilize deep learning algorithms to provide an automated solution for precise ventricular segmentation and volume quantification in a large dataset.

A large database of head computed tomographic (CT) scans was utilized to train and validate convoluted neural network models. Lateral ventricles were manually annotated in 103 scans and were randomly split with a training-validation ratio of 4:1. One U-net model was trained to segment the lateral ventricles in each slice, and another to segment the cranial vault. Each model was validated against the manually annotated images using the DICE index. Both networks were then used to segment and quantify the ventricular and cranial vault volumes of a large random sample from our database.

Both the ventricular volume and the cranial vault U-net models showed high fidelity to the manual annotation (DICE 0.909 and 0.983, respectively). They were then applied to a subset of 15,223 head CT scans from our database. Among these scans, 1,999 (13.1%) had a radiological report of cerebral atrophy and 1,404 (9.2%) of hydrocephalus. Age median was 40 years and 41.5% were females. Patients reported to have cerebral atrophy were confirmed by the U-nets to have larger ventricles. Cranial vault volume increased until the age group of 10-20, after which a plateau was observed. Conversely, ventricular volume increased with age without a plateau, evidently due to cerebral atrophy. Males had 13% larger cranial vault volumes than females. The ventricle-to-vault ratio showed no differences between the two sexes in younger ages, but men had a steeper increase with age than women.&nbsp;

This is the first study to measure ventricular volumes in such a large dataset, which has been made possible using artificial intelligence. Here we provide a robust method to establish normal values for ventricular volumes and a tool to routinely report these volumes on CT scans and evaluate for hydrocephalus.&nbsp;

Deep learning tools can provide ventricular volume measurements on head CTs efficiently and with high fidelity, and can assist in the diagnosis of hydrocephalus.&nbsp;

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Segmentation and Measurement of Ventricular and Cranial Vault Volumes in 15,223 Subjects Using Artificial Intelligence.webp

Segmentation and Measurement of Ventricular and Cranial Vault Volumes in 15,223 Subjects Using Artificial Intelligence

Area under receiver operating curve (AUC) is commonly used to evaluate and select artificial intelligence (AI) models for radiology. Artificially balanced/enriched datasets are also usually used to estimate AUC to maximize confidence interval to sample size ratio. In this work, we show that such evaluation of model performance has reached saturation and propose alternate performance evaluation schemes.

Receiver operating curve (ROC) is a curve where false positive rate (1 – specificity) and sensitivity of model at different thresholds are plotted on x and y-axes respectively. Similarly, precision recall curve (PRC) is plotted with recall (sensitivity) and precision (positive predictive value) on x and y-axes respectively. AUC is defined as area under ROC while average precision (AP) is defined as area under PRC. To illustrate the proposed evaluation scheme, two different high-performance models to detect fractures from head CT scans were created. In addition, two datasets were created by uniformly sampling scans and artificially enriching scans with fractures respectively. AUCs and APs were computed for the model-dataset pairs. We propose that AP computed on uniformly sampled dataset is more useful for model selection than other options.&nbsp;

AUCs for all four (model, dataset) pairs were &gt;92%. For both the datasets, difference in AUCs between the models was less than 3%. APs on enriched dataset were high for both models (95% &amp; 92% respectively). However, APs on uniformly sampled dataset were lower than expected (80% &amp; 69% respectively). The difference in models' performance was the highest (difference of 11%) when performance was measured using AP on uniformly sampled dataset.&nbsp;

AUC, although a commonly used performance metric for models, saturates early. Therefore, it is not suitable for model selection among high performance models (i.e. AUC &gt; 0.9). Similarly, model selection using artificially enriched datasets is not a good practice as both AUC and AP saturate early. Average precision measured on a uniformly sampled dataset shows the deficiencies in models' performance well and therefore, is a better metric for model selection.&nbsp;

Average precision and uniformly sampled datasets should be used to evaluate artificial intelligence models in radiology instead of AUC and enriched datasets.&nbsp;

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AUC and Enriched Datasets are Not Good Enough Anymore Presenting an Alternative Metric to Evaluate Radiology AI Models.webp

AUC and Enriched Datasets are Not Good Enough Anymore: Presenting an Alternative Metric to Evaluate Radiology AI Models

Pneumocephalus, accumulation of air in intracranial space, can lead to midline shift and compression of brain. In this work, we detail the development of deep learning algorithms for automated detection and localization of pneumocephalus in head CT scans.

Firstly, to localize the intracranial space from a given head CT scan, a skullÂ­-stripping algorithm was developed using a randomly sampled anonymized dataset of 78 head CT scans (1608 slices). We sampled another anonymized dataset containing 83 head CT scans (3546 slices) having pneumocephalus and 310 normal head CT scans which were randomly sampled to represent natural distribution. These 3546 slices (932 slices had pneumocephalus) were annotated for pneumocephalus regions. Then UÂ­Net based deep neural network algorithm was trained on these scans to accurately predict the pneumocephalus region . The predicted pneumocephalus region is refined by removing the regions outside the intracranial space identified by the skull stripping algorithm. The refined pneumocephalus region is then used to extract features. Using these features, a random forest was trained to classify the presence of pneumocephalus in a scan. Areas under receiver operating characteristics curves (AUC) were used to evaluate the algorithms.

An independent dataset of 1891 head CT scans (40 scans had pneumocephalus) was used for testing above algorithms. AUC for the scan level predictions was 0.89. Sensitivity and Specificity of 0.80 and 0.83 respectively were observed.

In this work, we showed the efficacy of deep learning algorithms in localizing and classifying the pneumocephalus accurately in a head CT scan.

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Automated Detection and Localization of Pneumocephalus in Head CT scan.webp

Automated Detection and Localization of Pneumocephalus in Head CT scan

The purpose of this study was to use a deep learning algorithm to detect and localize subacute and chronic ischemic infarcts on head CT scans for use in automated volumetric progression tracking.

We sampled 308 head CT scans (11840 slices) which were reported with chronic or subacute infarct. The infarcted regions in 11840 infarct-positive slices were marked. We trained segmentation algorithm to predict a heatmap of infarct lesion. The heatmap was used to derive scan level features representative of lesion density and volume to train a random forest to predict scan-level probabilities of chronic infarct. Area under receiver operating characteristics curves (AUC) were used to evaluate scan level predictions.

The algorithm was validated on an independent dataset of 1610 head CT scans containing 78 chronic &amp; 9 subacute infarct, 45 chronic ICH, 6 glioblastomas. The distribution of infarct affected territories was – 52.9% MCA, 33.3 % PCA, 9.3% ACA and 4.7% vertebrobasilar territories. The algorithm yielded AUC of 0.8474 (95% CI 0.7964 – 0.8984) for scan level predictions. It identified 8 of 9 subacute infarcts (88.89% recall) and 70 out of 78 chronic infarcts (89.74% recall). The eight missed chronic infarcts constituted of 3 lacunar and 2 hemorrhagic. The volumes of predicted infarct lesions ranged from 1 mL – 526 mL with mean prediction volume as 55.60mL.

The study demonstrates the capability of deep learning algorithms to accurately differentiate infarcts from infarct mimics.

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Deep Learning for Infarct Detection and Localization from Head CT Scans.webp

Deep Learning for Infarct Detection and Localization from Head CT Scans

To validate set of deep learning algorithms for automated detection of key findings from nonÂ­contrast head-CT scans: intracranial hemorrhage and its subtypes, calvarial fractures, midline shift and mass effect.

We retrospectively collected a dataset containing 313,318 head-CT scans of which random subset(Qure25k dataset) was used to validate and rest to develop algorithms. Additional dataset(CQ500 dataset) was collected from different centers to validate algorithms. Patients with postÂ­operative defect or age&lt;7 were excluded from all datasets. Three independent radiologists read each scan in CQ500 dataset. Original clinical radiology report and consensus of readers were considered as gold standards for Qure25k and CQ500 datasets respectively. Areas under receiver operating characteristics curves(AUCs) were used to evaluate algorithms.

After exclusion, Qure25k dataset contained 21,095 scans(mean-age 43;43% female) while CQ500 dataset consisted of 491(mean-age 48;36% female) scans. On Qure25k dataset, algorithms achieved an AUC of 0.92 for detecting intracranial hemorrhage(0.90-intraparenchymal, 0.96-intraventricular, 0.92-subdural, 0.93-extradural, and 0.90-subarachnoid hemorrhages). On CQ500 dataset, AUC was 0.94 for intracranial haemorrhage(0.95, 0.93, 0.95, 0.97, and 0.96 respectively). AUCs on Qure25k dataset were 0.92 for calvarial fractures, 0.93 for midline shift, and 0.86 for mass effect, while AUCs on CQ500 dataset were 0.96, 0.97 and 0.92 respectively.

This study demonstrates that deep learning algorithms can identify head-CT scan abnormalities requiring urgent attention with high AUCs.

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Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans.webp

Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans

Non-contrast Head-CT scans are primary imaging modality for evaluating patients with trauma or stroke. While results of deep learning algorithms to identify head-CT scans containing critical abnormalities has been published in retrospective studies, effects of deployment of such an algorithm in a real-world setting, with mobile notifications to clinician remain unstudied. In this prospective study, we evaluated performance of such an automated triage system in an urban 24-hour imaging facility.

We developed an accurate deep neural network algorithm that identifies and localizes intracranial bleeds, cranial fractures, mass effect and midline shift on non-contrast head-CT scans. The algorithm is deployed in clinical imaging facility in conjunction with an on-premise module that automatically selects eligible scans from PACS and uploads them to cloud-based algorithm for processing. Once processed, cloud algorithm returns an additional series, viewable as an overlay over the original, and a text notification to radiologist with preview images. Mobile notifications facilitated confirmation of the detected abnormalities. We studied the performance of the automated system over 60 days.

748 CT scans were taken over 60 days, of which 194 were non-contrast head-CT scans and these are evaluated by senior radiologist. Sensitivity, specificity, AUC and average time to notification of head-CT scans with critical abnormalities were Â&nbsp;0.90 (95% CI 0.74-0.98), 0.86 (0.80-0.91), 0.97 (0.92-1.00) and 3.2 minutes respectively.

An automated triage system in a radiology facility results in rapid notification of critical scans, with a low false positive rate and this may be used to expedite treatment initiation.

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Prospective evaluation of a deep learning algorithm deployed in an urban imaging centre to notify clinicians of head CT scans with critical abnormalities.webp

Prospective evaluation of a deep learning algorithm deployed in an urban imaging centre to notify clinicians of head CT scans with critical abnormalities

Mass effect and Midline shift are most critical and time­ sensitive abnormalities that can be readily detected on head-CT scan. We describe development and validation of deep learning algorithms to automatically detect the mentioned abnormalities.

We labeled slices from 699 anonymized nonÂ­contrast head-CT scans for the presence or absence of mass effect and midline shift in that slice. Number of scans(slices) with mass effect were 320(3143) and midline shift were 249(2074). We used these labels to train a modified ResNet18, a popular convolutional neural network to predict softmax based confidences for the presence of mass effect and midline shift in a slice. We modified the network by using two parallel fully connected(FC) layers in place of a single FC layer. The confidences at the slice­-level were combined using random forest to predict the scan-level confidence for the presence of mass effect and midline shift. A separate dataset(CQ500 dataset) was collected for the validation of the algorithm. Three senior radiologists independently read each scan in this dataset. Consensus of the readers’ opinion was used as the gold standard. We used areas under receiver operating characteristics curves(AUC) to evaluate the algorithm.

CQ500 dataset contained 491 scans of which number of scans with mass effect and midline shift were 99 and 47 respectively. AUC for detecting mass effect was 0.92(95%CI 0.89-0.95) and for detecting midline shift was 0.97(95%CI 0.94-0.99).

We show that a deep learning algorithm can be trained to accurately detect mass effect and midline shift from head CT scans.

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Automated Detection of Midline Shift and Mass Effect from Head CT Scans using Deep Learning.webp

Automated Detection of Midline Shift and Mass Effect from Head CT Scans using Deep Learning

Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms. We aimed to develop and validate a set of deep learning algorithms for automated detection of the following key findings from these scans: intracranial haemorrhage and its types (ie, intraparenchymal, intraventricular, subdural, extradural, and subarachnoid); calvarial fractures; midline shift; and mass effect.

We retrospectively collected a dataset containing 313 318 head CT scans together with their clinical reports from around 20 centres in India between Jan 1, 2011, and June 1, 2017. A randomly selected part of this dataset (Qure25k dataset) was used for validation and the rest was used to develop algorithms. An additional validation dataset (CQ500 dataset) was collected in two batches from centres that were di erent from those used for the development and Qure25k datasets. We excluded postoperative scans and scans of patients younger than 7 years. The original clinical radiology report and consensus of three independent radiologists were considered as gold standard for the Qure25k and CQ500 datasets, respectively. Areas under the receiver operating characteristic curves (AUCs) were primarily used to assess the algorithms.

The Qure25k dataset contained 21 095 scans (mean age 43 years; 9030 [43%] female patients), and the CQ500 dataset consisted of 214 scans in the rst batch (mean age 43 years; 94 [44%] female patients) and 277 scans in the second batch (mean age 52 years; 84 [30%] female patients). On the Qure25k dataset, the algorithms achieved an AUC of 0·92 (95% CI 0·91–0·93) for detecting intracranial haemorrhage (0·90 [0·89–0·91] for intraparenchymal, 0·96 [0·94–0·97] for intraventricular, 0·92 [0·90–0·93] for subdural, 0·93 [0·91–0·95] for extradural, and 0·90 [0·89–0·92] for subarachnoid). On the CQ500 dataset, AUC was 0·94 (0·92–0·97) for intracranial haemorrhage (0·95 [0·93–0·98], 0·93 [0·87–1·00], 0·95 [0·91–0·99], 0·97 [0·91–1·00], and 0·96 [0·92–0·99], respectively). AUCs on the Qure25k dataset were 0·92 (0·91–0·94) for calvarial fractures, 0·93 (0·91–0·94) for midline shift, and 0·86 (0·85–0·87) for mass e ect, while AUCs on the CQ500 dataset were 0·96 (0·92–1·00), 0·97 (0·94–1·00), and 0·92 (0·89–0·95), respectively.

Our results show that deep learning algorithms can accurately identify head CT scan abnormalities requiring urgent attention, opening up the possibility to use these algorithms to automate the triage process.

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Deep learning algorithms for detection of critical findings in head CT scans – A retrospective study.webp

Deep learning algorithms for detection of critical findings in head CT scans – A retrospective study

Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms.

To develop and validate a set of deep learning algorithms for automated detection of following key findings from non-contrast head CT scans: intracranial hemorrhage (ICH) and its types, intraparenchymal (IPH), intraventricular (IVH), subdural (SDH), ex- tradural (EDH) and subarachnoid (SAH) hemorrhages, calvarial fractures, midline shift and mass effect.

We retrospectively collected a dataset containing 313,318 head CT scans along with their clinical reports from various centers. A part of this dataset (Qure25k dataset) was used to validate and the rest to develop algorithms. Additionally, a dataset (CQ500 dataset) was collected from different centers in two batches B1 &amp; B2 to clinically validate the algorithms.

Original clinical radiology report and consensus of three independent radiologists were considered as gold standard for Qure25k and CQ500 datasets respectively. Area under receiver operating characteristics curve (AUC) for each finding was primarily used to evaluate the algorithms.

Qure25k dataset contained 21,095 scans (mean age 43.31; 42.87% female) while batches B1 and B2 of CQ500 dataset consisted of 214 (mean age 43.40; 43.92% female) and 277 (mean age 51.70; 30.31% female) scans respectively. On Qure25k dataset, the algorithms achieved AUCs of 0.9194, 0.8977, 0.9559, 0.9161, 0.9288 and 0.9044 for detecting ICH, IPH, IVH, SDH, EDH and SAH respectively. AUCs for the same on CQ500 dataset were 0.9419, 0.9544, 0.9310, 0.9521, 0.9731 and 0.9574 respectively. For detecting calvarial fractures, midline shift and mass effect, AUCs on Qure25k dataset were 0.9244, 0.9276 and 0.8583 respectively, while AUCs on CQ500 dataset were 0.9624, 0.9697 and 0.9216 respectively.

This study demonstrates that deep learning algorithms can accurately identify head CT scan abnormalities requiring urgent attention. This opens up the possibility to use these algorithms to automate the triage process. They may also provide a lower bound for quality and consistency of radiological interpretation.

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Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans.webp

Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans

To develop and validate a deep neural network-based algorithm for automated, rapid and accurate detection from head CT for the following haemorrhages: intracerebral (ICH), subdural (SDH), extradural (EDH) and subarachnoid (SAH).

An anonymised database of head CTs was searched for non-contrast scans which were reported with any of ICH, SDH, EDH, SAH and those which were reported with neither of these. Each slice of these scans is manually tagged with the haemorrhages that are visible in that slice. In all, 3040 scans (116227 slices) were annotated, of which number of scans(slices) with ICH, SDH, EDH, SAH and neither of these are 781(6957), 493(6593), 742(6880), 561(5609) and 944(92999), respectively. Our deep learning model is a modified ResNet18 with 4 parallel final fully connected layers for each of the haemorrhages. This model is trained on the slices from the annotated dataset to make slice-level decisions. Random forests are trained with ResNet’s softmax outputs for all the slices in a scan as features to make scan-level decisions.

A different set of 2993 scans, uniformly sampled from the database without any exclusion criterion, is used for testing the scan-level decisions. Number of scans with ICH, SDH, EDH and SAH in this set are 123, 58, 41 and 62, respectively. Area under the receiver operating curve (AUC) for scan-level decisions for ICH, SDH, EDH and SAH are 0.91, 0.90, 0.90 and 0.90, respectively. Algorithm takes less than 1s to produce the decision for a scan.

Deep learning can accurately detect intra- and extra-axial haemorrhages from head CTs.

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Automated detection of intra- and extra-axial haemorrhages on CT brain images using deep neural networks.webp

Automated detection of intra- and extra-axial haemorrhages on CT brain images using deep neural networks

To develop and validate deep learning-based algorithm pipeline for fast detection and localisation of skull fractures from non-contrast CT scans. All kinds of skull fractures: undisplaced, depressed, comminuted, etc. were included as part of study.

Anonymized and annotated dataset of 350 scans (11750 slices) with skull fractures were used for generating candidate proposals for fractures. Stacked network pipeline was used for candidate generation – a fully convolutional network for ROI generation and another deep convolutional network for ROI classification. Final ROI classification model (ResNet18) yielded fracture probabilities for candidates generated through the fully convolutional (UNet) network. Separate deep learning model was trained to detect haemorrhages on scan level which was used as proxy for clinical information. Fracture candidate features like size, probabilities, depth for top 5 most probable fracture candidates along with haemorrhage model confidence (phaemorrhage) were combined to train random forest classifier to detect fracture on scan level. In case of predicted fracture, most probable candidate(s) were used for localization.

Separate set of 2971 scans, uniformly sampled from database with no exclusion criterion, was used for testing scan-level decisions with 108 scans reported as skull fracture cases. To evaluate scan-level decisions for fractures, area under receiver operating curve (AUC-ROC) was calculated as 0.83 with phaemorrhage as feature and 0.72 without. Free receiver operating curve yielded 0.9 sensitivity at 2.85 false-positives-per-scan. Predictions on each patient takes &lt; 30s.

Deep learning-based pipeline can accurately detect and localize skull fractures. Pipeline can be used for triaging patients for presence of skull fractures.

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thumbnail_Automated detection and localisation of skull fractures from CT scans using deep learning.webp

Automated detection and localisation of skull fractures from CT scans using deep learning.webp

Automated detection and localisation of skull fractures from CT scans using deep learning

CT scans of brain are often the frontline investigations in acute conditions of the brain, particularly strokes. Treatment outcomes largely depend on a quick and accurate interpretation of CT scans. A vital feature to illustrate severity of damage is midline shift – indicating high intracerebral (IC) hemorrhage pressure, which can be fatal. Our study aims at designing a deep convolutional network for detection, fast segmentation and quantification of IC hemorrhage, devising an algorithm for midline shift measurement and identification of cerebral hemisphere affected by the detected hemorrhage.

The anonymized and annotated dataset had 39 CT scans of brain (16 with IC hemorrhage). A deep neural network was trained slice by slice to segment hemorrhage. The network has fully convolutional encoder and decoder with skip connections in between for better localization. 26 scans (589 slices) were used for training and 13 scans (282 slices) for validation. Features extracted from each patient’s complete IC hemorrhage segmentation output were used to train a decision tree for final diagnosis. Ideal midline was drawn using center of mass in bone window and anterior bone protrusion at the level of foramen of Monro. This along with asymmetry in tissue densities gave displaced midline and midline shift. Affected hemisphere was identified using displaced midline and hemorrhage’s center of mass. Accuracy was measured using receiver operating characteristic (ROC) curve and dice score.

100 scans were separately collected over 2 weeks and used for testing. 6 of them had hemorrhage. Sensitivity and specificity for the diagnosis of hemorrhage were 100% and 98.9% respectively. ROC analysis revealed area under curve of 0.994. Model took 3 seconds to segment one CT scan on average. The mean dice score for all test scans was 0.988 while it was 0.80 for the 6 scans with hemorrhage. Midline shift and affected hemisphere were both identified with 100% accuracy.

In this work, we trained a deep convolutional network to detect and quantify IC hemorrhage in brain CT scans. We also measured midline shift and identified the affected hemisphere. The processing pipeline was fully automatic.

Automated detection of hemorrhage and midline shift can help rapidly distinguish between ischemic and hemorrhagic strokes, enabling faster decision-making and treatment.

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Deep Neural Networks to Identify and Localize Intracerebral Hemorrhage and Midline Shift in CT Scans of Brain.webp

Deep Neural Networks to Identify and Localize Intracerebral Hemorrhage and Midline Shift in CT Scans of Brain

Tracking the progression of brain tumors is a challenging task, due to the slow growth rate and the combination of different tumor components, such as cysts, enhancing patterns, edema and necrosis. In this paper, we propose a Deep Neural Network based architecture that does automatic segmentation of brain tumor, and focuses on improving accuracy at the edges of these different classes. We show that enhancing the loss function to give more weight to the edge pixels significantly improves the neural network’s accuracy at classifying the boundaries. In the BRATS 2016 challenge, our submission placed third on the task of predicting progression for the complete tumor region.

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Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression.webp

Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression

The ISLES 2016 challenge aims to address two important aspects of Ischemic stroke lesion treatment prediction. The first aspect relates to segmenting the brain MRI to identify the areas with lesions and the second aspect relates to predicting the actual clinical outcome in terms of the patient’s degree of disability. The input data consists of acute MRI scans and additional clinical such as TICI scores, Time Since Stroke, and Time to Treatment.To address this challenge we take a deep-learning based approach. In particular, we first focus on the segmentation task and use an automatic segmentation model that consists of a Deep Neural Network (DNN). The DNN takes as input the MRI images and outputs the segmented image, automatically learning the latent underlying features during the training process. The DNN architectures we consider utilize many convolutional layers with small kernels, e.g., 3×3. This approach requires fewer parameters to estimate, and allows one to learn and generalize from the somewhat limited amount of data that is provided.One of the architectures we are currently utilizing is based on the U-Net [1], which is an all-convolutional network. It acts as an auto-encoder, that first “en- codes” the input image by applying combinations of convolutional and pooling operations. This is followed by the “decoding” step that up-scales the encoded images, while performing convolutions. The all-convolutional architecture of the U-Net allows it to handle input images of different dimensions as in the challenge dataset. In our experiments, we found that this architecture yielded excellent performance on the previous ISLES 2015 dataset. Although the modalities in the 2016 challenge are different, our initial training experiments have yielded promising segmentation results.Our next steps involve addressing the regression challenge. There is limited amount of labeled data for this task. Our approach will be to include these outcomes as part of the segmentation training directly. This will allow the DNN to learn latent features that can directly help with the classification task.

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thumbnail_A Deep-Learning Based Approach for Ischemic Stroke Lesion Outcome Prediction.webp

A Deep-Learning Based Approach for Ischemic Stroke Lesion Outcome Prediction.webp

A Deep-Learning Based Approach for Ischemic Stroke Lesion Outcome Prediction

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