Detection and classification of coronary artery calcifications in low dose thoracic CT using deep learning

Fuhrman, Jordan D.; Crosby, Jennie; Yip, Rowena; Henschke, Claudia I.; Yankelevitz, David F.; Giger, Maryellen L.

doi:10.1117/12.2513134

Cited by 6 publications

(8 citation statements)

References 8 publications

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“…AI techniques in the field of medicine are gaining more interest recently, but it should be recognized that initial efforts had started as early as in 1970s (12). Due to enhanced algorithms and computing power, AI has made great strides in many areas -classification of photos, person recognition, self-driving vehicles, natural language processing, and data mining, just to name a few and has generated great enthusiasm for streamlining cancer screening to improve early detection and diagnosis of cancer and personalize treatment and outcome prediction (72)(73)(74)(75)(76)(77)(78). The future of AI in lung cancer screening lies in the integration of algorithms that detect and diagnose all diseases visible in a LDCT, not only lung cancer but emphysema, interstitial lung disease, cardiovascular disease, and liver disease to automatically produce a report of everything that is visible on a LDCT chest scan.…”

Section: Integration Of Ai Approaches For Ldct Interpretationmentioning

confidence: 99%

Review of the future of lung cancer screening

Yip¹,

Jirapatnakul²,

Li³

et al. 2022

Curr Chall Thorac Surg

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Lung cancer screening has progressed greatly in the last two decades since the initial publication of ELCAP in 1999. Since then, large randomized trials, the NLST and NELSON studies, have also shown the benefit of lung cancer screening with CT, and it is now widely available. There are many areas for improving the benefit of screening by refining the eligibility criteria, continued optimization of the screening regimen, identifying other related diseases, and improved treatment. As artificial intelligence (AI) techniques continue to improve, an automated report of all diseases can be produced from the low-dose CT scan. New screening tests are being developed for lung cancer including blood-based tests. There is growing recognition that use of risk factor-based criteria which dichotomize age and pack-years of smoking to select eligible individuals for screening may not be the most efficient and may also lead to increased lung cancer disparity. Continued research will focus on optimal approaches for selecting high risk individuals for lung cancer screening and thus hopefully expand eligibility criteria beyond the current criteria which include only 20% of lung cancer patients. With the increase in early stage lung cancers identified from lung cancer screening, a key focus of ongoing research is the treatment of these cancers.

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Section: Integration Of Ai Approaches For Ldct Interpretationmentioning

confidence: 99%

Review of the future of lung cancer screening

Yip¹,

Jirapatnakul²,

Li³

et al. 2022

Curr Chall Thorac Surg

Self Cite

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“…Computer aided screening of calcification in radiological images is not specific only to the current topic. There are other procedures in which it can be adopted, such as for detecting coronary to classify coronary artery calcifications in low dose thoracic CT [31]. Other studies used a variety of deep neural network approaches based on CT images to predict different pathologies, such as transcatheter aortic valve replacement [32], chemotherapy response in breast cancer [33],…”

Section: Introductionmentioning

confidence: 99%

Deep convolution neural network for screening carotid calcification in dental panoramic radiographs

Amitay

Barnett-Itzhaki

Sudri

et al. 2022

Preprint

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Ischemic stroke, a leading global cause of death and disability, is caused by carotid arteries atherosclerosis. Such calcifications are classically detected by ultrasound screening. In recent years it was shown that these calcifications can also be inferred from routine panoramic dental radiographs. In this work, we focused on the panoramic dental radiographs taken from 500 patients, manually labelling each of the patients’ sides (each radiograph was treated as two sides), and which were used to develop an artificial intelligence (AI)-based algorithm to automatically detect carotid calcifications. The algorithm uses deep learning convolutional neural networks (CNN), with transfer learning (TL) approaches followed by eXtreme Gradient Boosting algorithm (XGBoost) that achieved true labels for each corner, and reaches a sensitivity (recall) of 0.82 and a specificity of 0.93 for individual artery, and a recall of 0. 8 8 and specificity of 0.86 for individual patients. Applying and integrating the algorithm we developed in healthcare units and dental clinics has the potential of reducing stroke events and their mortality and morbidity consequences.

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“…[92][93][94][95][96][97][98] CAD of cardiac and vascular imaging included coronary artery calcium scoring with deep learning, coronary artery detection, and stenosis analysis on angiography and CT, intravascular OCT, cardiomegaly assessment, and cardiac wall and chamber assessment. [99][100][101][102][103][104] Atherosclerotic disease outside the heart was also assessed. 105,106 CAD of brain imaging included detection, segmentation, and classification of brain tumors, Alzheimer's dementia, neonatal brain analysis, stroke outcome prediction, radiogenomics of glioblastoma, intracranial hemorrhage and aneurysms, hydrocephalus diagnosis, glioma mutation assessment, and traumatic brain injury.…”

Section: Introductionmentioning

confidence: 99%

SPIE Computer-Aided Diagnosis conference anniversary review

Summers

Giger

2022

J. Med. Imag.

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The SPIE Computer-Aided Diagnosis conference has been held for 16 consecutive years at the annual SPIE Medical Imaging symposium. The conference remains vibrant, with a core group of submitters as well as new submitters and attendees each year. Recent developments include a marked shift in submissions relating to the artificial intelligence revolution in medical image analysis. This review describes the topics and trends observed in research presented at the Computer-Aided Diagnosis conference as part of the 50th-anniversary celebration of SPIE Medical Imaging.

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Detection and classification of coronary artery calcifications in low dose thoracic CT using deep learning

Cited by 6 publications

References 8 publications

Review of the future of lung cancer screening

Review of the future of lung cancer screening

Deep convolution neural network for screening carotid calcification in dental panoramic radiographs

SPIE Computer-Aided Diagnosis conference anniversary review

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