Combat radiology folio investing

The work in Rothan and Byrareddy revealed that a significant number of people were infected from the wet animal market in Wuhan city, considered the zoonotic origin of the COVID Eventually, multiple cases got spread across China, and the world is giving it the status of a global outbreak Surveillances, The authors in Lu et al.

The work in Xu et al. Similarly, the study in Malik et al. Various applications have been developed using concepts of computer vision, ML, and image processing approach to monitor and control the spreading of COVID disease. Computer vision, image processing, and ML-based devices are being used for inspection, identification, gauging, or guiding of COVID As an example, protective gear, respirator, ventilators, automatic sanitizers are being used for treating patients and protecting healthcare professionals, ensuring virus containment as well.

Thermal screening is being used for measuring the temperature of individuals as elevated body temperature is a primary symptom. Social distancing is being implemented strictly to ensure safe distancing from affected patients and vision-guided robots are being used in this regard. The Australian government has launched Draganfly, an unmanned aerial vehicle UAV company, for immediate deployment of drones to detect COVID infections among people in remote locations.

Image processing and computer vision technologies are being used for the mass production of healthcare products and gears to be used by all stakeholders in the hospitals. These devices assist in avoiding the spreading of the virus by minimizing human contact. These technologies have proved their roles in diagnosing and reducing airborne virus particles, which has possibilities of infecting a large number of people. The key challenge of this evidence-based approach is to execute the model involving data collection, analysis and reporting in real-time.

It is possible to understand the impact and spread of COVID based on gender, geographical region, travel history, age, and daily updates from any such surveillance data using DL and ML approaches. The work in Pourghasemi et al. As far as data is concerned, the internet acts as a very useful source to gain a tremendous amount of information about the COVID virus. Apart from that COVID related information - the number of confirmed infectious cases, death tolls, and recoveries are also available at the Johns Hopkins University dashboard C.

Engineering CSSE , Aarogya Setu mobile app G. DL applications have been used with medical image processing approaches for the development and validation of a model at Renmin Hospital of Wuhan University in China Chen, Wu, et al. This model retrospectively collected 46, unidentified images of hospitalized patients. The admitted patients belonged to two categories wherein the first were COVID infected patients, and the later had other diseases.

A DL-based system was designed to ensure an easy decision for doctors to detect COVID instances of infected pneumonia early enough to control the epidemic. Coronavirus diagnosis and treatment Coronavirus is not a single virus but a group or family of multiple viruses. Once a patient is infected with coronavirus, the symptoms could be similar to normal cold infection or severe respiratory syndromes. In such a crisis, outbreak prediction models and virus-spread tracking tools that involve DL and medical image processing have huge potential for COVID diagnosis and treatment processes.

These tools help in supporting doctors for the initial screening process and rapid detection for accurate diagnosis of the disease. The role of technology is very important in the functioning of DL and medical image processing to combat COVID ensuring faster and accurate patient diagnosis. The authors in Li et al. Initially, tests were conducted taking throat swab and nasopharyngeal samples from a patient and the samples were used to collect RNA using specific chemical processes.

This RNN mixed with a specific reverse transcriptase enzyme i. The medical image processing techniques such as computerized tomography have always helped in fast and accurate diagnosis of diseases and it is no different in the case of COVID as well. Bai et al. DL and medical image processing play an important role in differentiating between COVID infected and non-infected patients. The hospitals in Spain consider this methodology as their default feature in a diagnostic pathway. However, other sources have identified X-ray as an alternative examination Chen, Zhou, et al.

Interestingly, the work in Peng, Wang, Zhang, and C. Group , Poggiali et al. The work in Jin et al. The guideline consists of the methodology, epidemiological characteristics, population prevention, diagnosis, treatment of COVID disease. Standford University has provided data, models, tools, research studies, and funding opportunities for COVID research.

In total, cases and controls were included in the analysis ref. Nash et al. Of all, 73 were diagnosed with TB. The Kodak Point-of-Care system was used to take chest radiographs with a resolution of x pixels. A total of individuals with a diagnosis of DM and Of CXRs, are normal and are abnormal cases. Medical Surveillance Dataset Yonsei University, South Korea : The annual medical surveillance data for workers at Yonsei University, beginning from was used to create this dataset of 39, individuals of which individuals have TB.

The images are available through the Open-i multimodal search engine ref. The images are acquired as a part of routine clinical care with IRB approvals. During the period, a total of 6, individuals with presumptive TB were enrolled. Of these, individuals, with invalid, error, no result were excluded from the analysis, and final dataset consists of 6, CXRs.

The sick but non-TB cases are gathered in order to cover as many types of radiograph diseases as possible in the clinical scenarios. Over 3. All participants above 15 years underwent screening. It includes 26, images of which 17, are abnormal and 8, are normal. Large image database consisting of 47, postero-anterior CXRs from 39, individuals was created. It has , frontal-view both postero-anterior and antero-posterior CXRs of 30, unique patients. Of , CXRs, 60, are normal and 51,, abnormal.

No further information about the dataset was provided but the data is available upon request from the corresponding author upon request. They are labeled for the presence of 14 observations as positive, negative, or uncertain. In our systematic review, we consider their methodologies and results in accordance with dataset size. Regarding dataset, we study and provide availability for research purpose as well as their respective sources previous section.

In our study, we also consider sate-of-the-art articles that used Covid and Pneumonia cases in addition to TB. Table 1 Dataset collections and their respective sources Full size table In Table 2 , starting from , we summarize our systematic review on DL-based imaging tools using CXR images , where we consider dataset size and performance that are commonly measured in accuracy, area under the curve, specificity and sensitivity.

On the whole, the average accuracy and AUC with transfer learning are 0. Melendez et al. Using fold cross validation, the reported accuracy when CAD scores and clinical information were combined was 0. The combined strategy offers improved accuracy and specificity. In their study, it is observed that the most valuable clinical features reported in the integrated approach are HIV status, auxiliary temperature and lung auscultation findings.

The radiologists augmented reported a sensitivity of 0. In their study, authors reported that there are 13 misclassifications out of test cases, and the study can be further enhanced to increase the accuracy in disagreement cases that radiologists correctly interpreted. Of all, using 5-fold cross validation, the best test AUC of 0. The idea was not just to improve the performance but also to check whether CNN architecture can be generalized.

The study concluded that CNN has relatively better generalization power, and authors reported that the proposed network outperformed all the other state-of-the-art works. Not just limited to binary decision, Rajpurkar et al. In addition, authors compared the investigating capabilities of the DL algorithm with radiologists. ChexNeXt employed a layer DenseNet architecture.

Each layer was directly connected to every other layer within a block, and for each layer, the feature maps of all preceding layers were used as inputs, and its own feature maps were passed on to all following layers as inputs. In their experiment, on a data collection C26 MD, USA , the suggested model, was at par with radiologists for 10 pathologies and performed less in three pathologies Cardiomegaly, Emphysema and Hernia but algorithmperformed better in detecting atelectasis.

In Melendez et al. The likelihood score calculated was compared for each CXR. In their experiments, they reported a specificity of 0. However, there validation protocol was not clear. In this work, the generated heatmap helps in highlighting regions-of-interest. Additionally, it provides explanations for assigned TB scores. However, the study claims that there is a preference for software in high burden and resource-constrained areas, and for triage, the analysis is performed on a low percentage of TB active cases.

Similarly, Zaidi et al. Their experiments were performed on a different data collection, C19 Pakistan of size 6, individuals with presumptive TB enrolled in centers of Pakistan. As in [ 43 ], the software calculated the likelihood score for each CXR, and a high score indicated a more severe abnormality score for each CXR.

However, their validation protocol was not clear. Bekar et al. Yadav et al. Their experiment analysis was not clear about validation approach. Further, they used a heat map or bounding box to point out abnormalities to the clinician, facilitating rapid confirmation, proving a valuable supplement to the existing healthcare systems. Expert was used as the reference standard.

In their experiments, AUCs are similar; 0. Ge et al. Hwa et al. The study reported an accuracy of The results showed that features extracted from different images could improve the detection rate, further focusing on types of features and TB classification based on severity level. It does not necessarily due to they need large amount of data for training; it could also potentially be the size of the architectures. In Pasa et al. Their CNN architecture was light. It consisted of 5 convolutional blocks, followed by a global average pooling layer which compressed each feature map to its mean value and a fully connected Softmax layer with two outputs.

The convolutions were all zero-padded to preserve the input resolution and each convolutional layer also made use of batch normalization to speed up the training procedure and reduce overfitting. Interestingly, authors took an additional step on data visualization. It used saliency maps and gradient weighted class activation method gradCAMs for an excellent visual explanation, and helped clinical officers better review and interpret. Their experiments followed 5-fold cross validation approach.

Authors did not report separate test results on C8 Belarus data collection. Ahsan et al. Integrating demographic factors with CXR data provides better and consistent decision-making. In Heo et al. The main demographic factors were age, weight, height, and gender.

It is always interesting to compare the performance of AI-guided tools in reading CXRs against radiologist readings. Philipsen et al. Using software, the reported AUC of the software is 0. In this study, it was not clear about their validation protocol. Even though the reported accuracy shows that the software had a slightly higher sensitivity than the physician, the study lacks measures of statistical significance. The performance of automated CXR is at par with physicians. In addition, examining them by radiologist another expensive task in the process.

In Kim et al. Radiologists had a sensitivity and specificity of 0. Rajpurkar et al. It used a layer DenseNet architecture to extract image features of size The network forked into two modules, one for TB diagnosis using the image features and the clinical covariates, and the other for predicting the occurrence of six clinical findings that were diagnosed by radiologists.

The TB module first used a linear layer to learn 20 image features from original feature vector of size , and then combined them with the 8 couples to feed the resulting dimensional patient representation into a two layer neural network to predict TB. During inference, each of the five algorithms in the ensemble produced a probability of TB and these probabilities were averaged to get a final, ensemble probability.

Sensitivity was 0. However, their validation protocol is not clear. Rather than investing on TB versus normal CXR classification, creating a large dataset by considering other pulmonary abnormalities is an open problem. In addition, reducing DNN architectures without degrading performance has been one of open challenges.

Das et al. The point of truncation was chosen experimentally, that yielded the best classification results.

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