A.k Dutta Anatomy Pdf

[Trisha Quercioli]

Thesite is secure.

The ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

A key element of contact lens practice involves clinical evaluation of anterior eye health, including the cornea and limbus, conjunctiva and sclera, eyelids and eyelashes, lacrimal system and tear film. This report reviews the fundamental anatomy and physiology of these structures, including the vascular supply, venous drainage, lymphatic drainage, sensory innervation, physiology and function. This is the foundation for considering the potential interactions with, and effects of, contact lens wear on the anterior eye. This information is not consistently published as academic research and this report provides a synthesis from all available sources. With respect to terminology, the report aims to promote the consistent use of nomenclature in the field, and generally adopts anatomical terms recommended by the Federative Committee for Anatomical Terminology. Techniques for the examination of the ocular surface are also discussed.

Background: The application of deep learning on medical imaging is growing in prevalence in the recent literature. One of the most studied areas is coronary artery disease (CAD). Imaging of coronary artery anatomy is fundamental, which has led to a high number of publications describing a variety of techniques. The aim of this systematic review is to review the evidence behind the accuracy of deep learning applications in coronary anatomy imaging.

Methods: The search for the relevant studies, which applied deep learning on coronary anatomy imaging, was performed in a systematic approach on MEDLINE and EMBASE databases, followed by reviewing of abstracts and full texts. The data from the final studies was retrieved using data extraction forms. A meta-analysis was performed on a subgroup of studies, which looked at fractional flow reserve (FFR) prediction. Heterogeneity was tested using tau2, I2 and Q tests. Finally, a risk of bias was performed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) approach.

Conclusions: Deep learning has been used in many applications on coronary anatomy imaging, most of which are yet to be externally validated and prepared for clinical use. The performance of deep learning, especially CNN models, proved to be powerful and some applications have already translated into medical practice, such as computed tomography (CT)-FFR. These applications have the potential to translate technology into better care of CAD patients.

Coronary artery disease (CAD) is considered a leading cause of death and hospitalisation in high-income countries, and worldwide (1). The progressive nature of coronary atherosclerosis is the main underlying pathological process. Therefore, it is essential to have timely diagnosis of CAD to aid the management of patients and reduce both morbidity and mortality.

The last two decades have witnessed significant advancements in CAD imaging, from functional assessment of coronary artery stenoses and how they impact on the myocardium at stress and rest, using cardiac magnetic resonance (CMR), myocardial perfusion scintigraphy (MPS), and echocardiography, to anatomical assessment by means of coronary computed tomography angiography (CCTA) and invasive X-rays coronary angiography.

Computer vision technology on the other hand is going through an exciting era following the revolution of deep learning and artificial intelligence (AI) algorithms. CAD imaging is one of the key applications which has been targeted by many computer vision experts and deep learning practitioners.

There has been an explosion in the number of deep learning publications in CAD over the recent years with a focus on atherosclerosis and coronary anatomy imaging. The wide range of methodology presented in the recent literature opened the door for applications in various coronary artery imaging modalities.

This review aims to unravel this challenge by summarising the new information we gained so far in this field, evaluating the performance of the presented deep learning algorithms, and drawing some conclusions on potential meaningful applications. We present the following article in accordance with the PRISMA reporting checklist (available at -22-36/rc).

This review follows the Cochrane Review structure of diagnostic test accuracy (DTA) (2). The umbrella protocol for this systematic review is registered in the International Prospective Register of Systematic Reviews (PROSPERO, CRD42020204164), and reported according to PRISMA guidelines. All searching activities were performed by two independent reviewers (EA and UD), with divergences solved after consensus.

Without restrictions on minimal sample sizes or recruitment process, both prospective and retrospective studies were included. The included studies had participants with known or suspected CAD who had atherosclerosis imaging (invasive and non-invasive) with the application of deep learning technology, and compared with the gold standard (reference) test used in clinical practice.

Competitions presented in conferences on deep learning techniques, such as at the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference, animal studies, and simulation studies were not included due to ambiguity in their direct relation to patient care. Studies which used atherosclerosis data as a target for outcome prediction were excluded, as were studies, which focused on clinical data and imaging reports rather than imaging data for prediction. Studies, which used deep learning software with no details on the deep learning architecture were also excluded. Fusion imaging studies were not part of this review, and studies of automated coronary anatomy and atherosclerosis quantification, which relied mainly on hand crafted or non-learning algorithms were not included.

For fractional flow reserve (FFR) derived from CCTA using deep learning, only the original publications were included in this review, all subsequent publications, which used the same algorithms for different clinical applications were considered external validation papers and were not included in this review.

The performance of deep learning models was measured with various metrics including sensitivity, specificity, area under the curve (AUC), precision, recall, F1 score, Dice coefficient, Jaccard coefficient, and correlation. Those metrics were described quantitatively.

Data were reported as count or percentages. The pooled values of some of the reported diagnostic accuracy after the application of deep learning models, which were part of the meta-analysis, were visualised by forest plots.

A confusion matrix was produced for each of the included studies in meta-analysis given that most studies did not report the true negative (TN), true positive (TP), false negative (FN), and false positive (FP) values. This was calculated by taking sample size (S) to calculate FN from sensitivity, and FP from specificity. The TN and TP were then calculated from total sample size S.

Meta-analysis was performed on studies, which reported the same outputs with the corresponding sensitivity and specificity. Since pooling sensitivities or specificities can be misleading, the diagnostic odds ratio (DOR) approach is taken to calculate the pooled diagnostic performance. The fixed effect case of Mantel-Haenszel (MH) method is used.

The most popular imaging modality in deep learning application was CCTA (58%), as shown in Figure 2. However, invasive coronary angiography has gained more interest in recent years, along with invasive coronary intra-vascular imaging [optical coherence tomography (OCT) and intravascular ultrasound (IVUS)], which have been a focus for deep learning applications in recent years. Both OCT and IVUS are performed during invasive coronary angiography to add more detailed imaging analysis of atherosclerotic lesions seen on Cine X-ray images.

The most commonly used deep learning technique was convolutional neural network (CNN) as shown in Figure 3, with more than half of the studies (52%) have used this approach as a single model or combined with other models. The use of multi-layer perceptron (MLP) was scarce with only 4 studies reported their results using MLP approach. There was a variety of models used with only a few studies in each category, including generative adversarial network (GAN), recurrent neural network (RNN), random forest (RF), gradient boost, support vector machine (SVM), to name a few.

Several CCTA studies have focused on detection or quantification of coronary calcium given its prognostic importance in clinical outcomes. There have been successful applications of deep learning models using mainly CNNs to detect coronary artery calcification (CAC). Studies with large sample sizes have been conducted and reported good or excellent model performance in detecting CAC. Huo et al. (43) used 2,332 of scan-rescan pairs as input to their CNN architecture called AID-Net, which is composed of 3D ResNet and 3D DenseNet layers. They reported high model performance with AUC as high as 0.93 in detecting CAC. van Velzen et al. (63) used a large sample of CCTA data from 7,240 participants, and with a CNN they quantified CAC and achieved a high model performance with 97% inter-class correlation with expert reader and 96% accuracy. All other studies had smaller sample sizes and reported similar level of performance for CAC detection and quantification using CNNs.

Fischer et al. (62) used RNN for CAC quantification, and their model achieved good performance with sensitivity of 92% and specificity of 89%. All these reports confirm that deep learning algorithms are capable of performing CAC detection or rule out, and quantification in a highly reliable way and with less time than an expert human reader.

All of the four main imaging modalities (CCTA, OCT, IVUS, invasive coronary angiography) were used for deep learning applications to assess coronary stenosis in various ways: coronary plaque classification and segmentation, coronary stenosis classification and segmentation, culprit lesions predictors, vulnerable plaque precursors, thrombus, dissection and clinical outcome prediction.

3a8082e126