Machine learning for diagnosis of coronary artery disease in computed tomography angiography: A survey

Coronary artery disease (CAD) has become a major illness endangering human health. It mainly manifests as atherosclerotic plaques, especially vulnerable plaques without obvious symptoms in the early stage. Once a rupture occurs, it will lead to severe coronary stenosis, which in turn may trigger a major adverse cardiovascular event. Computed tomography angiography (CTA) has become a standard diagnostic tool for early screening of coronary plaque and stenosis due to its advantages in high resolution, noninvasiveness, and three-dimensional imaging. However, manual examination of CTA images by radiologists has been proven to be tedious and time-consuming, which might also lead to intra- and interobserver errors. Nowadays, many machine learning algorithms have enabled the (semi-)automatic diagnosis of CAD by extracting quantitative features from CTA images. This paper provides a survey of these machine learning algorithms for the diagnosis of CAD in CTA images, including coronary artery extraction, coronary plaque detection, vulnerable plaque identification, and coronary stenosis assessment. Most included articles were published within this decade and are found in the Web of Science. We wish to give readers a glimpse of the current status, challenges, and perspectives of these machine learning-based analysis methods for automatic CAD diagnosis.

Acoustic concept based on an autonomous capsule and a wideband concentric ring resonator for pathophysiological prevention

BACKGROUND

Research on the performance of elements constituting our modern environment is constantly evolving, both on a daily basis and on technological basis. But to date, the response of the system to the expectations of the population remains too modest.

AIM

To elaborate an ultrasonic technique to scan and evaluate in-vivo physiological properties by coupling sensors and multilayer biological tissues model.

METHODS

A low-frequency ultrasonic method (around a frequency of 32 KHz) based on the use of an innovative autonomous ultrasonic capsule as a miniaturized elementary spherical sensor (1 cm of diameter) and micro-rings resonators were examined.

RESULTS

Other their functions as passive listeners for the prevention and diagnosis in physiopathology of the respiratory and laryngeal apparatus, these micro-resonators coupled to the ultrasonic capsule through biological tissues (the body) are capable of evaluating the effects of aggression of the environment on human metabolism.

CONCLUSION

This would allow consequently the detection of some potential diseases at an early stage, even in people who still represent no symptoms, which would permit an early treatment and a higher chance of cure.

Acute pancreatitis: A pictorial review of early pancreatic fluid collections

Acute pancreatitis is a common acute inflammatory disease involving the pancreas and peripancreatic tissues or remote organs. The revised Atlanta classification 2012 of acute pancreatitis divides patients into mild, moderately severe and severe groups. Major changes of the classification include acute fluid collection terminology. However, some inappropriate terms of the radiological diagnosis reports in the daily clinical work or available literature may still be found. The aim of this review article is: to present an image-rich overview of different morphologic characteristics of the early-stage (within 4 wk after symptom onset) local complications associated with acute pancreatitis by computed tomography or magnetic resonance imaging; to clarify confusing imaging concepts for pancreatic fluid collections and underline standardised reporting nomenclature; to assist communication among treating physicians; and to facilitate the implications for clinical management decision-making.

Cerebral amyloid angiopathy vs Alzheimer’s dementia: Diagnostic conundrum

BACKGROUND

Diagnosis of a dementia subtype can be complex and often requires comprehensive cognitive assessment and dedicated neuroimaging. Clinicians are prone to cognitive biases when reviewing such images. We present a case of cognitive impairment and demonstrate that initial imaging may have resulted in misleading the diagnosis due to such cognitive biases.

CASE SUMMARY

A 76-year-old man with no cognitive impairment presented with acute onset word finding difficulty with unremarkable blood tests and neurological examination. Magnetic resonance imaging (MRI) demonstrated multiple foci of periventricular and subcortical microhaemorrhage, consistent with cerebral amyloid angiopathy (CAA). Cognitive assessment of this patient demonstrated marked impairment mainly in verbal fluency and memory. However, processing speed and executive function are most affected in CAA, whereas episodic memory is relatively preserved, unlike in other causes of cognitive impairment, such as Alzheimer’s dementia (AD). This raised the question of an underlying diagnosis of dementia. Repeat MRI with dedicated coronal views demonstrated mesial temporal lobe atrophy which is consistent with AD.

CONCLUSION

MRI brain can occasionally result in diagnostic overshadowing, and the application of artificial intelligence to medical imaging may overcome such cognitive biases.

Artificial intelligence in pancreatic disease

In recent years, the application of artificial intelligence (AI) in radiology has been growing rapidly, fueled by the availability of large datasets, advances in computing power, and newly developed algorithms. Progress in AI applied to medical imaging analyses has transformed these images into quantitative data, termed radiomics. When combined with patients’ clinical data, these models, when developed by machine learning, have the potential to improve diagnostic, prognostic, and predictive accuracy. Currently, limited literature is available on the use of radiomics for pancreatic disease. Here, we will review recent studies in the application of AI in a variety of pancreatic diseases, mainly involving lesion detection, tumor characterization, tumor grading, response, and prognosis evaluation. Finally, we will also discuss the challenges and prospects in the field of radiomics for pancreatic disease.

Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art

Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI.

Rising role of artificial intelligence in image reconstruction for biomedical imaging

In this editorial, we review recent progress on the applications of artificial intelligence (AI) in image reconstruction for biomedical imaging. Because it abandons prior information of traditional artificial design and adopts a completely data-driven mode to obtain deeper prior information via learning, AI technology plays an increasingly important role in biomedical image reconstruction. The combination of AI technology and the biomedical image reconstruction method has become a hotspot in the field. Favoring AI, the performance of biomedical image reconstruction has been improved in terms of accuracy, resolution, imaging speed, etc. We specifically focus on how to use AI technology to improve the performance of biomedical image reconstruction, and propose possible future directions in this field.