Computer-aided detection of pulmonary pathology in pediatric chest radiographs

Towards Accurate Point-of-Care Tests for Tuberculosis in Children

In childhood tuberculosis (TB), with an estimated 69% of missed cases in children under 5 years of age, the case detection gap is larger than in other age groups, mainly due to its paucibacillary nature and children’s difficulties in delivering sputum specimens. Accurate and accessible point-of-care tests (POCTs) are needed to detect TB disease in children and, in turn, reduce TB-related morbidity and mortality in this vulnerable population. In recent years, several POCTs for TB have been developed. These include new tools to improve the detection of TB in respiratory and gastric samples, such as molecular detection of Mycobacterium tuberculosis using loop-mediated isothermal amplification (LAMP) and portable polymerase chain reaction (PCR)-based GeneXpert. In addition, the urine-based detection of lipoarabinomannan (LAM), as well as imaging modalities through point-of-care ultrasonography (POCUS), are currently the POCTs in use. Further to this, artificial intelligence-based interpretation of ultrasound imaging and radiography is now integrated into computer-aided detection products. In the future, portable radiography may become more widely available, and robotics-supported ultrasound imaging is currently being trialed. Finally, novel blood-based tests evaluating the immune response using “omic-“techniques are underway. This approach, including transcriptomics, metabolomic, proteomics, lipidomics and genomics, is still distant from being translated into POCT formats, but the digital development may rapidly enhance innovation in this field. Despite these significant advances, TB-POCT development and implementation remains challenged by the lack of standard ways to access non-sputum-based samples, the need to differentiate TB infection from disease and to gain acceptance for novel testing strategies specific to the conditions and settings of use.

Computational Approach to Combat COVID-19 Infection: Emerging Tool for Accelerating Drug Research

BACKGROUND

Drug discovery and development process is an expensive, complex, time-consuming and risky. There are different techniques involved in the drug development process which include random screening, computational approach, molecular manipulation and serendipitous research. Among these methods, the computational approach is considered as an efficient strategy to accelerate and economize the drug discovery process.

OBJECTIVE

This approach is mainly applied in various phases of drug discovery process including target identification, target validation, lead identification and lead optimization. Due to increase in the availability of information regarding various biological targets of different disease states, computational approaches such as molecular docking, de novo design, molecular similarity calculation, virtual screening, pharmacophore-based modeling and pharmacophore mapping have been applied extensively.

METHODS

Various drug molecules can be designed by applying computational tools to explore the drug candidates for treatment of Coronavirus infection. The world health organization has announced the novel corona virus disease as COVID-19 and declared it as pandemic globally on 11 February 2020. So, it is thought of interest to scientific community to apply computational methods to design and optimize the pharmacological properties of various clinically available and FDA approved drugs such as remdesivir, ribavirin, favipiravir, oseltamivir, ritonavir, arbidol, chloroquine, hydroxychloroquine, carfilzomib, baraticinib, prulifloxacin, etc for effective treatment of COVID-19 infection.

RESULTS

Further, various survey reports suggest that the extensive studies are carried out by various research communities to find out the safety and efficacy profile of these drug candidates.

CONCLUSION

This review is focused on the study of various aspects of these drugs related to their target sites on virus, binding interactions, physicochemical properties etc.

Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

Artificial intelligence (AI) applications for chest radiography and chest CT are among the most developed applications in radiology. More than 40 certified AI products are available for chest radiography or chest CT. These AI products cover a wide range of abnormalities, including pneumonia, pneumothorax and lung cancer. Most applications are aimed at detecting disease, complemented by products that characterize or quantify tissue. At present, none of the thoracic AI products is specifically designed for the pediatric population. However, some products developed to detect tuberculosis in adults are also applicable to children. Software is under development to detect early changes of cystic fibrosis on chest CT, which could be an interesting application for pediatric radiology. In this review, we give an overview of current AI products in thoracic radiology and cover recent literature about AI in chest radiography, with a focus on pediatric radiology. We also discuss possible pediatric applications.

Could automated analysis of chest X-rays detect early bronchiectasis in children?

Non-cystic fibrosis bronchiectasis is increasingly described in the paediatric population. While diagnosis is by high-resolution chest computed tomography (CT), chest X-rays (CXRs) remain a first-line investigation. CXRs are currently insensitive in their detection of bronchiectasis. We aim to determine if quantitative digital analysis allows CT features of bronchiectasis to be detected in contemporaneously taken CXRs. Regions of radiologically (A) normal, (B) severe bronchiectasis, (C) mild airway dilation and (D) other parenchymal abnormalities were identified in CT and mapped to corresponding CXR. An artificial neural network (ANN) algorithm was used to characterise regions of classes A, B, C and D. The algorithm was then tested in 13 subjects and compared to CT scan features. Structural changes in CT were reflected in CXR, including mild airway dilation. The areas under the receiver operator curve for ANN feature detection were 0.74 (class A), 0.71 (class B), 0.76 (class C) and 0.86 (class D). CXR analysis identified CT measures of abnormality with a better correlation than standard radiological scoring at the 99% confidence level.Conclusion: Regional abnormalities can be detected by digital analysis of CXR, which may provide a low-cost and readily available tool to indicate the need for diagnostic CT and for ongoing disease monitoring. What is Known: • Bronchiectasis is a severe chronic respiratory disorder increasingly recognised in paediatric populations. • Diagnostic computed tomography imaging is often requested only after several chest X-ray investigations. What is New: • We show that a digital analysis of chest X-ray could provide more accurate identification of bronchiectasis features.

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

BACKGROUND

Artificial intelligence (AI) and the Internet of Intelligent Things (IIoT) are promising technologies to prevent the concerningly rapid spread of coronavirus disease (COVID-19) and to maximize safety during the pandemic. With the exponential increase in the number of COVID-19 patients, it is highly possible that physicians and health care workers will not be able to treat all cases. Thus, computer scientists can contribute to the fight against COVID-19 by introducing more intelligent solutions to achieve rapid control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease.

OBJECTIVE

The objectives of this review were to analyze the current literature, discuss the applicability of reported ideas for using AI to prevent and control COVID-19, and build a comprehensive view of how current systems may be useful in particular areas. This may be of great help to many health care administrators, computer scientists, and policy makers worldwide.

METHODS

We conducted an electronic search of articles in the MEDLINE, Google Scholar, Embase, and Web of Knowledge databases to formulate a comprehensive review that summarizes different categories of the most recently reported AI-based approaches to prevent and control the spread of COVID-19.

RESULTS

Our search identified the 10 most recent AI approaches that were suggested to provide the best solutions for maximizing safety and preventing the spread of COVID-19. These approaches included detection of suspected cases, large-scale screening, monitoring, interactions with experimental therapies, pneumonia screening, use of the IIoT for data and information gathering and integration, resource allocation, predictions, modeling and simulation, and robotics for medical quarantine.

CONCLUSIONS

We found few or almost no studies regarding the use of AI to examine COVID-19 interactions with experimental therapies, the use of AI for resource allocation to COVID-19 patients, or the use of AI and the IIoT for COVID-19 data and information gathering/integration. Moreover, the adoption of other approaches, including use of AI for COVID-19 prediction, use of AI for COVID-19 modeling and simulation, and use of AI robotics for medical quarantine, should be further emphasized by researchers because these important approaches lack sufficient numbers of studies. Therefore, we recommend that computer scientists focus on these approaches, which are still not being adequately addressed.

Computer-aided diagnosis for World Health Organization-defined chest radiograph primary-endpoint pneumonia in children

BACKGROUND

The chest radiograph is the most common imaging modality to assess childhood pneumonia. It has been used in epidemiological and vaccine efficacy/effectiveness studies on childhood pneumonia.

OBJECTIVE

To develop computer-aided diagnosis (CAD4Kids) for chest radiography in children and to evaluate its accuracy in identifying World Health Organization (WHO)-defined chest radiograph primary-endpoint pneumonia compared to a consensus interpretation.

MATERIALS AND METHODS

Chest radiographs were independently evaluated by three radiologists based on WHO criteria. Automatic lung field segmentation was followed by manual inspection and correction, training, feature extraction and classification. Radiographs were filtered with Gaussian derivatives on multiple scales, extracting texture features to classify each pixel in the lung region. To obtain an image score, the 95^th percentile score of the pixels was used. Training and testing were done in 10-fold cross validation.

RESULTS

The radiologist majority consensus reading of 858 interpretable chest radiographs included 333 (39%) categorised as primary-endpoint pneumonia, 208 (24%) as other infiltrate only and 317 (37%) as no primary-endpoint pneumonia or other infiltrate. Compared to the reference radiologist consensus reading, CAD4Kids had an area under the receiver operator characteristic (ROC) curve of 0.850 (95% confidence interval [CI] 0.823-0.876), with a sensitivity of 76% and specificity of 80% for identifying primary-endpoint pneumonia on chest radiograph. Furthermore, the ROC curve was 0.810 (95% CI 0.772-0.846) for CAD4Kids identifying primary-endpoint pneumonia compared to other infiltrate only.

CONCLUSION

Further development of the CAD4Kids software and validation in multicentre studies are important for future research on computer-aided diagnosis and artificial intelligence in paediatric radiology.

A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis

We undertook a systematic review of the diagnostic accuracy of artificial intelligence-based software for identification of radiologic abnormalities (computer-aided detection, or CAD) compatible with pulmonary tuberculosis on chest x-rays (CXRs). We searched four databases for articles published between January 2005-February 2019. We summarized data on CAD type, study design, and diagnostic accuracy. We assessed risk of bias with QUADAS-2. We included 53 of the 4712 articles reviewed: 40 focused on CAD design methods (“Development” studies) and 13 focused on evaluation of CAD (“Clinical” studies). Meta-analyses were not performed due to methodological differences. Development studies were more likely to use CXR databases with greater potential for bias as compared to Clinical studies. Areas under the receiver operating characteristic curve (median AUC [IQR]) were significantly higher: in Development studies AUC: 0.88 [0.82–0.90]) versus Clinical studies (0.75 [0.66–0.87]; p-value 0.004); and with deep-learning (0.91 [0.88–0.99]) versus machine-learning (0.82 [0.75–0.89]; p = 0.001). We conclude that CAD programs are promising, but the majority of work thus far has been on development rather than clinical evaluation. We provide concrete suggestions on what study design elements should be improved.

A review on lung boundary detection in chest X-rays

PURPOSE

Chest radiography is the most common imaging modality for pulmonary diseases. Due to its wide usage, there is a rich literature addressing automated detection of cardiopulmonary diseases in digital chest X-rays (CXRs). One of the essential steps for automated analysis of CXRs is localizing the relevant region of interest, i.e., isolating lung region from other less relevant parts, for applying decision-making algorithms there. This article provides an overview of the recent literature on lung boundary detection in CXR images.

METHODS

We review the leading lung segmentation algorithms proposed in period 2006-2017. First, we present a review of articles for posterior-anterior view CXRs. Then, we mention studies which operate on lateral views. We pay particular attention to works that focus their efforts on deformed lungs and pediatric cases. We also highlight the radiographic measures extracted from lung boundary and their use in automatically detecting cardiopulmonary abnormalities. Finally, we identify challenges in dataset curation and expert delineation process, and we listed publicly available CXR datasets.

RESULTS

(1) We classified algorithms into four categories: rule-based, pixel classification-based, model-based, hybrid, and deep learning-based algorithms. Based on the reviewed articles, hybrid methods and deep learning-based methods surpass the algorithms in other classes and have segmentation performance as good as inter-observer performance. However, they require long training process and pose high computational complexity. (2) We found that most of the algorithms in the literature are evaluated on posterior-anterior view adult CXRs with a healthy lung anatomy appearance without considering challenges in abnormal CXRs. (3) We also found that there are limited studies for pediatric CXRs. The lung appearance in pediatrics, especially in infant cases, deviates from adult lung appearance due to the pediatric development stages. Moreover, pediatric CXRs are noisier than adult CXRs due to interference by other objects, such as someone holding the child's arms or the child's body, and irregular body pose. Therefore, lung boundary detection algorithms developed on adult CXRs may not perform accurately in pediatric cases and need additional constraints suitable for pediatric CXR imaging characteristics. (4) We have also stated that one of the main challenges in medical image analysis is accessing the suitable datasets. We listed benchmark CXR datasets for developing and evaluating the lung boundary algorithms. However, the number of CXR images with reference boundaries is limited due to the cumbersome but necessary process of expert boundary delineation.

CONCLUSIONS

A reliable computer-aided diagnosis system would need to support a greater variety of lung and background appearance. To our knowledge, algorithms in the literature are evaluated on posterior-anterior view adult CXRs with a healthy lung anatomy appearance, without considering ambiguous lung silhouettes due to pathological deformities, anatomical alterations due to misaligned body positioning, patient's development stage and gross background noises such as holding hands, jewelry, patient's head and legs in CXR. Considering all the challenges which are not very well addressed in the literature, developing lung boundary detection algorithms that are robust to such interference remains a challenging task. We believe that a broad review of lung region detection algorithms would be useful for researchers working in the field of automated detection/diagnosis algorithms for lung/heart pathologies in CXRs.