Classification models for SPECT myocardial perfusion imaging

Berkaya, Selcan Kaplan; Sivrikoz, İlknur Ak; Günal, Serkan

doi:10.1016/j.compbiomed.2020.103893

Cited by 36 publications

(39 citation statements)

References 24 publications

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“…In the scope of Machine Learning-based (ML) techniques, the major part of recent studies use CCTA characteristics as reference [3,5,21,28,34,39,45,52], but most of them also were evaluated with a reduced number of patients and arteries [5,21,28,33,34,42,50]. These researches use geometrical lesion data [3,21,28,39,42,45,52], clinical risk scores [39] and anatomical descriptors [34].…”

Section: Conducting the Search Strategymentioning

confidence: 99%

“…The major part of the ML-based techniques proposed in recent studies follow the image-based approach [16,21,28,31,33,42,45,54] using (deep) neural networks, which typically require large databases to be properly trained. This need for a huge amount of data associated with the lack of openly available datasets has also led to synthetically generated databases [21].…”

Section: Gq2 -What Are the Most Promising Techniques In The Coronary Functional Assessment?mentioning

confidence: 99%

“…The plaque characteristics are mainly used in ML-based techniques and are challenging to reproduce [3,28,71]. For this reason, we noticed the increasing use of transfer learning (TL) approaches to leverage the networks performance [33,45] and the creation of a complete sequence of steps to stenosis detection [54]. TL is the process of improving the target predictive function by using the related information from an already trained function, improving a small part of the network parameters and reusing the initial layers, which represent the most generic features [75].…”

Section: Gq2 -What Are the Most Promising Techniques In The Coronary Functional Assessment?mentioning

confidence: 99%

“…In the image-based context, region of interest (ROI) [42], features segmentation [16,33,43] and data reduction [8] are the most regarded elements. These studies suggest that an image is not enough to determine the lesion significance yet [8,16,33,42,43].…”

Section: Gq2 -What Are the Most Promising Techniques In The Coronary Functional Assessment?mentioning

confidence: 99%

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Functional Coronary Artery Assessment: a Systematic Literature Review

Freitas

Nienow

Costa

et al. 2021

Wien Klin Wochenschr

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Cardiovascular diseases represent the number one cause of death in the world, including the most common disorders in the heart's health, namely coronary artery disease (CAD). CAD is mainly caused by fat accumulated in the arteries' internal walls, creating an atherosclerotic plaque that impacts the blood flow functional behavior. Anatomical plaque characteristics are essential but not sufficient for a complete functional assessment of CAD. In fact, plaque analysis and visual inspection alone have proven insufficient to determine the lesion severity and hemodynamic repercussion. Furthermore, the fractional flow reserve (FFR) exam, which is considered the gold standard for stenosis functional impair determination, is invasive and contains several limitations. Such a panorama evidences the need for new techniques applied to image exams to improve CAD functional assessment. In this article, we perform a systematic literature review on emerging methods determining CAD significance, thus delivering a unique base for comparing these methods, qualitatively and quantitatively. Our goal is to guide further studies with evidence from the most promising methods, highlighting the benefits from both areas. We summarize benchmarks,

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Section: Conducting the Search Strategymentioning

confidence: 99%

Section: Gq2 -What Are the Most Promising Techniques In The Coronary Functional Assessment?mentioning

confidence: 99%

Section: Gq2 -What Are the Most Promising Techniques In The Coronary Functional Assessment?mentioning

confidence: 99%

Section: Gq2 -What Are the Most Promising Techniques In The Coronary Functional Assessment?mentioning

confidence: 99%

See 2 more Smart Citations

Functional Coronary Artery Assessment: a Systematic Literature Review

Freitas

Nienow

Costa

et al. 2021

Wien Klin Wochenschr

View full text Add to dashboard Cite

show abstract

“…Traditional ML methods have been widely used in medicine for a long time, including naive Bayes, support vector machines, and random forests. The applications of ML in nuclear medicine imaging include disease diagnosis [positron emission tomography (PET) (15), single-photon emission computed tomography (SPECT) (16,17)], prognosis [PET (18), SPECT (19)], lesion classification [PET (20), SPECT (21,22)], and imaging physics (23). In recent years, DL technologies such as CNNs, artificial neural networks (ANNs), and generative adversarial networks (GANs) have developed very fast and shown better performance than traditional ML in some cases.…”

Section: Introductionmentioning

confidence: 99%

Applications of artificial intelligence in nuclear medicine image generation

Cheng¹,

Wen²,

Huang³

et al. 2021

Quant Imaging Med Surg

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Recently, the application of artificial intelligence (AI) in medical imaging (including nuclear medicine imaging) has rapidly developed. Most AI applications in nuclear medicine imaging have focused on the diagnosis, treatment monitoring, and correlation analyses with pathology or specific gene mutation.It can also be used for image generation to shorten the time of image acquisition, reduce the dose of injected tracer, and enhance image quality. This work provides an overview of the application of AI in image generation for single-photon emission computed tomography (SPECT) and positron emission tomography (PET) either without or with anatomical information [CT or magnetic resonance imaging (MRI)]. This review focused on four aspects, including imaging physics, image reconstruction, image postprocessing, and internal dosimetry. AI application in generating attenuation map, estimating scatter events, boosting image quality, and predicting internal dose map is summarized and discussed.

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Case Studies on X-ray Imaging, MRI and Nuclear Imaging

Sarker,

Biswas,

et al. 2023

Data Driven Approaches on Medical Imaging

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Classification models for SPECT myocardial perfusion imaging

Cited by 36 publications

References 24 publications

Functional Coronary Artery Assessment: a Systematic Literature Review

Functional Coronary Artery Assessment: a Systematic Literature Review

Applications of artificial intelligence in nuclear medicine image generation

Case Studies on X-ray Imaging, MRI and Nuclear Imaging

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