Detecting Intracranial Hemorrhage with Deep Learning

Majumdar, Arjun; Brattain, Laura J.; Telfer, Brian A.; Farris, Chad; Scalera, Jonathan

doi:10.1109/embc.2018.8512336

Cited by 67 publications

(23 citation statements)

References 18 publications

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“…In clinical diagnostics, the first applications of AI in healthcare to be cleared by the US Food and Drug Administration (FDA) have been dominated by applications of computer vision to medical scans (for example, magnetic resonance imaging (MRI) or positron emission tomography images), and pathology images (for example, histopathological slides). The first medical imaging applications include the automated quantification of blood flow through the heart via cardiac MRI [7], the determination of ejection fraction from echocardiograms [8], the detection and volumetric quantification of lung nodules from radiographs [7], the detection and quantification of breast densities via mammography [9], the detection of stroke, brain bleeds, and other conditions from computerized axial tomography [10, 11], and automated screening for diabetic retinopathy from comprehensive dilated eye examination [12, 13]. Imaging applications in pathology include an FDA-cleared system for whole-slide imaging [14], and promising approaches to the automated classification of dermatological conditions [15], as well as numerous other whole-slide imaging and AI systems in development that are expected to dramatically enhance the efficiency of pathologists [16].…”

Section: Artificial Intelligence and Its Applicationsmentioning

confidence: 99%

Artificial intelligence in clinical and genomic diagnostics

2019

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Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.

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Section: Artificial Intelligence and Its Applicationsmentioning

confidence: 99%

Artificial intelligence in clinical and genomic diagnostics

2019

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“…In reality, however, some studies have findings that require prompt action and therefore should be prioritized. Recently, a deep learning-based triage system that detects free gas, free fluid, or fat stranding in abdominal CTs was published (27), and multiple studies have already demonstrated the feasibility of detecting critical findings in head CT scans (28)(29)(30)(31). In the future, such systems could work directly on raw data, such as sinograms, and raise alerts during the scan time, even before reconstruction.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Nuclear Medicine

2019

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Despite the great media attention for artificial intelligence (AI), for many health care professionals the term and the functioning of AI remain a "black box," leading to exaggerated expectations on the one hand and unfounded fears on the other. In this review, we provide a conceptual classification and a brief summary of the technical fundamentals of AI. Possible applications are discussed on the basis of a typical work flow in medical imaging, grouped by planning, scanning, interpretation, and reporting. The main limitations of current AI techniques, such as issues with interpretability or the need for large amounts of annotated data, are briefly addressed. Finally, we highlight the possible impact of AI on the nuclear medicine profession, the associated challenges and, last but not least, the opportunities.

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“…Dawud et al [ 45 ] showed that a finely tuned and pre-trained AlexNet-SVM model can enhance a deep learning model for hematoma detection. Majumdar et al [ 89 ] proposed a modified U-Net model to classify four subtypes of hematoma. Lee et al [ 90 ] reported an ensemble model comprised of VGG16, ResNet-50, Inception-v3, and Inception-ResNet-v2 for the localisation and classification of five hematoma types.…”

Section: Generics Of Computer Aided Diagnosismentioning

confidence: 99%

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Vidhya

Gudigar

Raghavendra

et al. 2021

IJERPH

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Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.

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Detecting Intracranial Hemorrhage with Deep Learning

Cited by 67 publications

References 18 publications

Artificial intelligence in clinical and genomic diagnostics

Artificial intelligence in clinical and genomic diagnostics

Artificial Intelligence in Nuclear Medicine

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

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