An East Coast Perspective on Artificial Intelligence and Machine Learning

Gupta, Rajiv; Krishnam, Sanjith Prahas; Schaefer, Pamela W.; Lev, Michael H.; González, Ramón

doi:10.1016/j.nic.2020.08.002

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…[37][38][39]43,[45][46][47] Downstream applications of AI in neuroradiology and neurology include using CT to aid in detecting hemorrhage or ischemic stroke; using MRI to automatically segment lesions, such as tumors or MS lesions; assisting in early diagnosis and predicting prognosis in MS; assisting in treating paralysis, including from spinal cord injury; determining seizure type and localizing area of seizure onset; and using cameras, wearable devices, and smartphone applications to diagnose and assess treatment response in neurodegenerative disorders, such as Parkinson or Alzheimer diseases (Figure). 37,[48][49][50][51][52][53][54][55][56] Several AI tools have been deployed in the clinical setting, particularly triaging intracranial hemorrhage and moving these studies to the top of the radiologist's worklist. In 2020 the Centers for Medicare and Medicaid Services (CMS) began reimbursing Viz.ai software's AI-based Viz ContaCT (Viz LVO) with a new International Statistical Classification of Diseases, Tenth Revision procedure code.…”

Section: Reducing Contrast and Radiation Dosesmentioning

confidence: 99%

Neuroimaging in the Era of Artificial Intelligence: Current Applications

Monsour¹

2022

Federal Practitioner

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Background: Artificial intelligence (AI) in medicine has shown significant promise, particularly in neuroimaging. AI increases efficiency and reduces errors, making it a valuable resource for physicians. With the increasing amount of data processing and image interpretation required, the ability to use AI to augment and aid the radiologist could improve the quality of patient care. Observations: AI can predict patient wait times, which may allow more efficient patient scheduling. Additionally, AI can save time for repeat magnetic resonance neuroimaging and reduce the time spent during imaging. AI has the ability to read computed tomography, magnetic resonance imaging, and positron emission tomography with reduced or without contrast without significant loss in sensitivity for detecting lesions. Neuroimaging does raise important ethical considerations and is subject to bias. It is vital that users understand the practical and ethical considerations of the technology. Conclusions: The demonstrated applications of AI in neuroimaging are numerous and varied, and it is reasonable to assume that its implementation will increase as the technology matures. AI's use for detecting neurologic conditions holds promise in combatting ever increasing imaging volumes and providing timely diagnoses.

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Section: Reducing Contrast and Radiation Dosesmentioning

confidence: 99%

Neuroimaging in the Era of Artificial Intelligence: Current Applications

Monsour¹

2022

Federal Practitioner

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“…Multisequence MRI plays a pivotal role in the treatment of liver cancer, and the features of different sequence images can be complementary. Since traditional manual segmentation of lesions has many shortcomings, there is an urgent need for methods to automatically and accurately identify and segment HCC lesions to help doctors diagnose and improve efficiency [21,22]. erefore, U-Net was improved and DFN and data enhancement strategies were introduced.…”

Section: Discussionmentioning

confidence: 99%

Automatic Segmentation Algorithm of Magnetic Resonance Image in Diagnosis of Liver Cancer Patients under Deep Convolutional Neural Network

Zhang

Zhao

2021

Scientific Programming

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To study the influence of different sequences of magnetic resonance imaging (MRI) images on the segmentation of hepatocellular carcinoma (HCC) lesions, the U-Net was improved. Moreover, deep fusion network (DFN), data enhancement strategy, and random data (RD) strategy were introduced, and a multisequence MRI image segmentation algorithm based on DFN was proposed. The segmentation experiments of single-sequence MRI image and multisequence MRI image were designed, and the segmentation result of single-sequence MRI image was compared with those of convolutional neural network (FCN) algorithm. In addition, RD experiment and single-input experiment were also designed. It was found that the sensitivity (0.595 ± 0.145) and DSC (0.587 ± 0.113) obtained by improved U-Net were significantly higher than the sensitivity (0.405 ± 0.098) and DSC (0.468 ± 0.115, P < 0.05 ) obtained by U-Net. The sensitivity of multisequence MRI image segmentation algorithm based on DFN (0.779 ± 0.015) was significantly higher than that of FCN algorithm (0.604 ± 0.056, P < 0.05 ). The multisequence MRI image segmentation algorithm based on the DFN had higher indicators for liver cancer lesions than those of the improved U-Net. When RD was added, it not only increased the DSC of the single-sequence network enhanced by the hepatocyte-specific magnetic resonance contrast agent (Gd-EOB-DTPA) by 1% but also increased the DSC of the multisequence MRI image segmentation algorithm based on DFN by 7.6%. In short, the improved U-Net can significantly improve the recognition rate of small lesions in liver cancer patients. The addition of RD strategy improved the segmentation indicators of liver cancer lesions of the DFN and can fuse image features of multiple sequences, thereby improving the accuracy of lesion segmentation.

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“…The increasing burden on radiological departments is not predicted to be backed with an equivalent increase in radiologists and it is therefore highly likely that increased MRI use will lead to longer response times or increased error rates [9,10]. To counterbalance this for stroke diagnosis, artificial intelligence (AI) has been proposed as a technology to enhance the radiology workflow [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis

Bojsen,

Elhakim,

Graumann

et al. 2024

Insights Imaging

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Objectives This systematic review and meta-analysis aimed to assess the stroke detection performance of artificial intelligence (AI) in magnetic resonance imaging (MRI), and additionally to identify reporting insufficiencies. Methods PRISMA guidelines were followed. MEDLINE, Embase, Cochrane Central, and IEEE Xplore were searched for studies utilising MRI and AI for stroke detection. The protocol was prospectively registered with PROSPERO (CRD42021289748). Sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curve were the primary outcomes. Only studies using MRI in adults were included. The intervention was AI for stroke detection with ischaemic and haemorrhagic stroke in separate categories. Any manual labelling was used as a comparator. A modified QUADAS-2 tool was used for bias assessment. The minimum information about clinical artificial intelligence modelling (MI-CLAIM) checklist was used to assess reporting insufficiencies. Meta-analyses were performed for sensitivity, specificity, and hierarchical summary ROC (HSROC) on low risk of bias studies. Results Thirty-three studies were eligible for inclusion. Fifteen studies had a low risk of bias. Low-risk studies were better for reporting MI-CLAIM items. Only one study examined a CE-approved AI algorithm. Forest plots revealed detection sensitivity and specificity of 93% and 93% with identical performance in the HSROC analysis and positive and negative likelihood ratios of 12.6 and 0.079. Conclusion Current AI technology can detect ischaemic stroke in MRI. There is a need for further validation of haemorrhagic detection. The clinical usability of AI stroke detection in MRI is yet to be investigated. Critical relevance statement This first meta-analysis concludes that AI, utilising diffusion-weighted MRI sequences, can accurately aid the detection of ischaemic brain lesions and its clinical utility is ready to be uncovered in clinical trials. Key Points There is a growing interest in AI solutions for detection aid. The performance is unknown for MRI stroke assessment. AI detection sensitivity and specificity were 93% and 93% for ischaemic lesions. There is limited evidence for the detection of patients with haemorrhagic lesions. AI can accurately detect patients with ischaemic stroke in MRI. Graphical Abstract

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An East Coast Perspective on Artificial Intelligence and Machine Learning

Cited by 13 publications

References 21 publications

Neuroimaging in the Era of Artificial Intelligence: Current Applications

Neuroimaging in the Era of Artificial Intelligence: Current Applications

Automatic Segmentation Algorithm of Magnetic Resonance Image in Diagnosis of Liver Cancer Patients under Deep Convolutional Neural Network

Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis

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