OBJECTIVES
To evaluate the evidence for the effectiveness and cost-effectiveness of the newer diagnostic imaging tests as an addition to clinical examination and patient history for the diagnosis of soft tissue shoulder disorders.


DATA SOURCES
Literature was identified from several sources including general medical databases.


REVIEW METHODS
Studies were identified that evaluated clinical examination, ultrasound, magnetic resonance imaging (MRI), or magnetic resonance arthrography (MRA) in patients suspected of having soft tissue shoulder disorders. Outcomes assessed were clinical impingement syndrome or rotator cuff tear (full, partial or any). Only cohort studies were included. The methodological quality of included test accuracy studies was assessed using a formal quality assessment tool for diagnostic studies and the extraction of study findings was conducted in duplicate using a pre-designed and piloted data extraction form to avoid any errors. For each test, sensitivity, specificity and positive and negative likelihood ratios with 95% confidence intervals were calculated for each study. Where possible pooled estimates of sensitivity, specificity and likelihood ratios were calculated using random effects methods. Potential sources of heterogeneity were investigated by conducting subgroup analyses.


RESULTS
In the included studies, the prevalence of rotator cuff disorders was generally high, partial verification of patients was common and in many cases patients who were selected retrospectively because they had undergone the reference test. Sample sizes were generally very small. Reference tests were often inappropriate with many studies using arthrography alone, despite problems with its sensitivity. For clinical assessment, 10 cohort studies were found that examined either the accuracy of individual tests or clinical examination as a whole: individual tests were either good at ruling out rotator cuff tears when negative (high sensitivity) or at ruling in such disorders when positive (high specificity), but small sample sizes meant that there was no conclusive evidence. Ultrasound was investigated in 38 cohort studies and found to be most accurate when used for the detection of full-thickness tears; sensitivity was lower for detection of partial-thickness tears. For MRI, 29 cohort studies were included. For full-thickness tears, overall pooled sensitivities and specificities were fairly high and the studies were not statistically heterogeneous; however for the detection of partial-thickness rotator cuff tears, the pooled sensitivity estimate was much lower. The results from six MRA studies suggested that it may be very accurate for detection of full-thickness rotator cuff tears, although its performance for the detection of partial-thickness tears was less consistent. Direct evidence for the performance of one test compared with another is very limited.


CONCLUSIONS
The results suggest that clinical examination by specialists can rule out the presence of a rotator cuff tear, and that either MRI or ultrasound could equally be used for detection of full-thickness rotator cuff tears, although ultrasound may be better at picking up partial tears. Ultrasound also may be more cost-effective in a specialist hospital setting for identification of full-thickness tears. Further research suggestions include the need for large, well-designed, prospective studies of the diagnosis of shoulder pain, in particular a follow-up study of patients with shoulder pain in primary care and a prospective cohort study of clinical examination, ultrasound and MRI, alone and/or in combination.

The effectiveness of diagnostic tests for the assessment of shoulder pain due to soft tissue disorders

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis.

Automatic pterygium detection is an essential screening tool for health community service groups. It allows non-expert to perform screening process without the needs of big and expensive equipment, especially for the application in rural areas. Thence, patients who have been screened as positive pterygium will be referred to the certified medical personnel for further diagnosis and treatment. Current state-of-the-art algorithms for pterygium detection rely on basic machine learning approach such as artificial neural network and support vector machine, which have not yet achieved high detection sensitivity and specificity as required in standard medical practice. Hence, a deep learning approach based on fully convolutional neural networks is proposed to detect and localize the pterygium infected tissues automatically. The input image requirement for the developed system is low as any commercial mobile phone camera is sufficient. Moreover, the developed algorithm, which we refer as Pterygium-Net works well even if the eye image is captured under low lighting condition with pupil position is not at the center location. Pterygium-Net utilizes three layers of convolutional neural networks (CNN) and three layers of fully connected networks. Two steps are implemented to overcome lacks of training data by generating synthetic images and pre-training the CNN weights and biases in a different public dataset. As for pterygium localization, an additional step of box proposal based on edges information is used to generate possible regions of the pterygium infected tissues. Hanning window is also applied to the generated regions to give more weightage to the center area. Experimental results show that Pterygium-Net produces high average detection sensitivity and specificity of 0.95 and 0.983, respectively. As for pterygium tissues localization, the algorithm achieves 0.811 accuracy with a very low failure rate of 0.053. In the future, deeper networks can be implemented to further improve pterygium localization.

Pterygium-Net: a deep learning approach to pterygium detection and localization

The direct analysis of 3D Optical Coherence Tomography (OCT) volumes enables deep learning models (DL) to learn spatial structural information and discover new bio-markers that are relevant to glaucoma. Downsampling 3D input volumes is the state-of-art solution to accommodate for the limited number of training volumes as well as the available computing resources. However, this limits the network's ability to learn from small retinal structures in OCT volumes. In this paper, our goal is to improve the performance by providing guidance to DL model during training in order to learn from finer ocular structures in 3D OCT volumes. Therefore, we propose an end-to-end attention guided 3D DL model for glaucoma detection and estimating visual function from retinal structures. The model consists of three pathways with the same network architecture but different inputs. One input is the original 3D-OCT cube and the other two are computed during training guided by the 3D gradient class activation heatmaps. Each pathway outputs the class-label and the whole model is trained concurrently to minimize the sum of losses from three pathways. The final output is obtained by fusing the predictions of the three pathways. Also, to explore the robustness and generalizability of the proposed model, we apply the model on a classification task for glaucoma detection as well as a regression task to estimate visual field index (VFI) (a value between 0 and 100). A 5-fold cross-validation with a total of 3782 and 10,370 OCT scans is used to train and evaluate the classification and regression models, respectively. The glaucoma detection model achieved an area under the curve (AUC) of 93.8% compared with 86.8% for a baseline model without the attention-guided component. The model also outperformed six different feature based machine learning approaches that use scanner computed measurements for training. Further, we also assessed the contribution of different retinal layers that are relevant to glaucoma. The VFI estimation model achieved a Pearson correlation and median absolute error of 0.75 and 3.6%, respectively, for a test set of size 3100 cubes.

Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images

Glaucoma is a leading eye disease, causing vision loss by gradually affecting peripheral vision if left untreated. Current diagnosis of glaucoma is performed by ophthalmologists, human experts who typically need to analyze different types of medical images generated by different types of medical equipment: fundus, Retinal Nerve Fiber Layer (RNFL), Optical Coherence Tomography (OCT) disc, OCT macula, perimetry, and/or perimetry deviation. Capturing and analyzing these medical images is labor intensive and time consuming. In this paper, we present a novel approach for glaucoma diagnosis and localization, only relying on fundus images that are analyzed by making use of state-of-the-art deep learning techniques. Specifically, our approach towards glaucoma diagnosis and localization leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), respectively. We built and evaluated different predictive models using a large set of fundus images, collected and labeled by ophthalmologists at Samsung Medical Center (SMC). Our experimental results demonstrate that our most effective predictive model is able to achieve a high diagnosis accuracy of 96%, as well as a high sensitivity of 96% and a high specificity of 100% for Dataset-Optic Disc (OD), a set of center-cropped fundus images highlighting the optic disc. Furthermore, we present Medinoid, a publicly-available prototype web application for computer-aided diagnosis and localization of glaucoma, integrating our most effective predictive model in its back-end.

https://biblio.ugent.be/publication/8627928/file/8627929.pdf

Medinoid : computer-aided diagnosis and localization of glaucoma using deep learning

Glaucoma is a major eye disease, leading to vision loss without proper medical treatment. Current diagnosis of glaucoma is performed by ophthalmologists who are typically analyzing different types of medical images generated by different types of medical equipment. However, capturing and analyzing these medical images is labor intensive and expensive. In this paper, we present a novel computational approach towards glaucoma diagnosis and localization, only making use of eye fundus images that are analyzed by state-of-the-art deep learning techniques. Specifically, our approach leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) for glaucoma diagnosis and localization, respectively. Quantitative and qualitative results, as obtained for a small-sized dataset with no segmentation ground truth, demonstrate that the proposed approach is promising, for instance achieving an accuracy of 0.91 and an ROC-AUC score of 0.92 for the diagnosis task.

Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning

Different algorithms for object dimension measurement have recently been proposed, leveraging the power of RGB-D cameras. However, most of these algorithms are not suitable for deployment in the logistics industry: they are only able to deal with non-moving objects and/or the usage of multiple cameras introduces time delays. In this paper, we introduce Box-Scan, a novel algorithm that enables real-time box dimension measurement in conveyor systems using a single RGB-D camera. We discuss the industrial setting in which our algorithm needs to operate, as well as a prototype that integrates the proposed algorithm. Furthermore, we provide an analysis of the effectiveness of our prototype as a function of the conveyor speed, demonstrating that the prototype built comes with a maximum measurement error of less than 5% at a conveyor speed of 3.4 km/h.

/pdf/box-scan-an-efficient-and-effective-algorithm-for-box-15y90w0qrh.pdf

Box-Scan: An efficient and effective algorithm for box dimension measurement in conveyor systems using a single RGB-D camera

Abstract RNA–protein interactions are crucial for diverse biological processes. In prokaryotes, RNA–protein interactions enable adaptive immunity through CRISPR-Cas systems. These defence systems utilize CRISPR RNA (crRNA) templates acquired from past infections to destroy foreign genetic elements through crRNA-mediated nuclease activities of Cas proteins. Thanks to the programmability and specificity of CRISPR-Cas systems, CRISPR-based antimicrobials have the potential to be repurposed as new types of antibiotics. Unlike traditional antibiotics, these CRISPR-based antimicrobials can be designed to target specific bacteria and minimize detrimental effects on the human microbiome during antibacterial therapy. In this study, we explore the potential of CRISPR-based antimicrobials by optimizing the RNA–protein interactions of crRNAs and Cas13 proteins. CRISPR-Cas13 systems are unique as they degrade specific foreign RNAs using the crRNA template, which leads to non-specific RNase activities and cell cycle arrest. We show that a high proportion of the Cas13 systems have no colocalized CRISPR arrays, and the lack of direct association between crRNAs and Cas proteins may result in suboptimal RNA–protein interactions in the current tools. Here, we investigate the RNA–protein interactions of the Cas13-based systems by curating the validation dataset of Cas13 protein and CRISPR repeat pairs that are experimentally validated to interact, and the candidate dataset of CRISPR repeats that reside on the same genome as the currently known Cas13 proteins. To find optimal CRISPR-Cas13 interactions, we first validate the 3-D structure prediction of crRNAs based on their experimental structures. Next, we test a number of RNA–protein interaction programs to optimize the in silico docking of crRNAs with the Cas13 proteins. From this optimized pipeline, we find a number of candidate crRNAs that have comparable or better in silico docking with the Cas13 proteins of the current tools. This study fully automatizes the in silico optimization of RNA–protein interactions as an efficient preliminary step for designing effective CRISPR-Cas13-based antimicrobials. 

/pdf/in-silico-optimization-of-rna-protein-interactions-for-2ju1h6w5.pdf

In silico optimization of RNA–protein interactions for CRISPR-Cas13-based antimicrobials

Glaucoma is a major eye disease, leading to vision loss in the absence of proper medical treatment. Current diagnosis of glaucoma is performed by ophthalmologists who are often analyzing several types of medical images generated by different types of medical equipment. Capturing and analyzing these medical images is labor-intensive and expensive. In this paper, we present a novel computational approach towards glaucoma diagnosis and localization, only making use of eye fundus images that are analyzed by state-of-the-art deep learning techniques. Specifically, our approach leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) for glaucoma diagnosis and localization, respectively. Quantitative and qualitative results, as obtained for a small-sized dataset with no segmentation ground truth, demonstrate that the proposed approach is promising, for instance achieving an accuracy of 0.91$\pm0.02$ and an ROC-AUC score of 0.94 for the diagnosis task. Furthermore, we present a publicly available prototype web application that integrates our predictive model, with the goal of making effective glaucoma diagnosis available to a wide audience.

Web Applicable Computer-aided Diagnosis of Glaucoma Using Deep Learning

https://biblio.ugent.be/publication/8606439/file/8606440.pdf

Ho-min Park

Papers

Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning

Box-Scan: An efficient and effective algorithm for box dimension measurement in conveyor systems using a single RGB-D camera

In silico optimization of RNA–protein interactions for CRISPR-Cas13-based antimicrobials

Web Applicable Computer-aided Diagnosis of Glaucoma Using Deep Learning

Web applicable computer-aided diagnosis of glaucoma using deep learning