Automatic Detection of Tuberculosis in Chest Radiographs Using a Combination of Textural, Focal, and Shape Abnormality Analysis

Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging

The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.

AI for Detection of Tuberculosis: Implications for Global Health

Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the diagnosis of tuberculosis. Computer-aided diagnosis (CAD) systems for tuberculosis detection on chest radiographs have recently made substantial progress in diagnostic performance, thanks to deep learning technologies. The current performance of CAD systems for tuberculosis has approximated that of human experts, presenting a potential solution to the shortage of human readers to interpret chest radiographs in low- or middle-income, high-tuberculosis-burden countries. This article provides a critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging. It also explores several considerations to scale up CAD solutions, encompassing manufacturer-independent CAD validation, economic and political aspects, and ethical concerns, as well as the potential for broadening radiography-based diagnosis to other nontuberculosis diseases. Collectively, CAD for tuberculosis will emerge as a representative deep learning application, catalyzing advances in global health and health equity. Keywords: Computer-aided Diagnosis (CAD), Conventional Radiography, Thorax, Lung, Machine Learning Supplemental material is available for this article. © RSNA, 2024.

A hybrid approach for automatic segmentation and classification to detect tuberculosis

Objective

Tuberculosis (TB) remains a significant global infectious disease, posing a considerable health threat, particularly in resource-constrained regions. Due to diverse datasets, radiologists face challenges in accurately diagnosing TB using X-ray images. This study aims to propose an innovative approach leveraging image processing techniques to enhance TB diagnostic accuracy within the automatic segmentation and classification (AuSC) framework for healthcare.

Methods

The AuSC of detection of TB (AuSC-DTB) framework comprises several steps: image preprocessing involving resizing and median filtering, segmentation using the random walker algorithm, and feature extraction utilizing local binary pattern and histogram of gradient descriptors. The extracted features are then classified using the support vector machine classifier to distinguish between healthy and infected chest X-ray images. The effectiveness of the proposed technique was evaluated using four distinct datasets, such as Japanese Society of Radiological Technology (JSRT), Montgomery, National Library of Medicine (NLM), and Shenzhen.

Results

Experimental results demonstrate promising outcomes, with accuracy rates of 94%, 95%, 95%, and 93% achieved for JSRT, Montgomery, NLM, and Shenzhen datasets, respectively. Comparative analysis against recent studies indicates superior performance of the proposed hybrid approach.

Conclusions

The presented hybrid approach within the AuSC framework showcases improved diagnostic accuracy for TB detection from diverse X-ray image datasets. Furthermore, this methodology holds promise for generalizing other diseases diagnosed through X-ray imaging. It can be adapted with computed tomography scans and magnetic resonance imaging images, extending its applicability in healthcare diagnostics.

Perceptions of Artificial Intelligence-Assisted Care for Children With a Respiratory Complaint

OBJECTIVES

To evaluate caregiver opinions on the use of artificial intelligence (AI)-assisted medical decision-making for children with a respiratory complaint in the emergency department (ED).

METHODS

We surveyed a sample of caregivers of children presenting to a pediatric ED with a respiratory complaint. We assessed caregiver opinions with respect to AI, defined as "specialized computer programs" that "help make decisions about the best way to care for children." We performed multivariable logistic regression to identify factors associated with discomfort with AI-assisted decision-making.

RESULTS

Of 279 caregivers who were approached, 254 (91.0%) participated. Most indicated they would want to know if AI was being used for their child's health care (93.5%) and were extremely or somewhat comfortable with the use of AI in deciding the need for blood (87.9%) and viral testing (87.6%), interpreting chest radiography (84.6%), and determining need for hospitalization (78.9%). In multivariable analysis, caregiver age of 30 to 37 years (adjusted odds ratio [aOR] 3.67, 95% confidence interval [CI] 1.43-9.38; relative to 18-29 years) and a diagnosis of bronchospasm (aOR 5.77, 95% CI 1.24-30.28 relative to asthma) were associated with greater discomfort with AI. Caregivers with children being admitted to the hospital (aOR 0.23, 95% CI 0.09-0.50) had less discomfort with AI.

CONCLUSIONS

Caregivers were receptive toward the use of AI-assisted decision-making. Some subgroups (caregivers aged 30-37 years with children discharged from the ED) demonstrated greater discomfort with AI. Engaging with these subgroups should be considered when developing AI applications for acute care.

Incidental radiological findings during clinical tuberculosis screening in Lesotho and South Africa: a case series

BACKGROUND

Chest X-ray offers high sensitivity and acceptable specificity as a tuberculosis screening tool, but in areas with a high burden of tuberculosis, there is often a lack of radiological expertise to interpret chest X-ray. Computer-aided detection systems based on artificial intelligence are therefore increasingly used to screen for tuberculosis-related abnormalities on digital chest radiographies. The CAD4TB software has previously been shown to demonstrate high sensitivity for chest X-ray tuberculosis-related abnormalities, but it is not yet calibrated for the detection of non-tuberculosis abnormalities. When screening for tuberculosis, users of computer-aided detection need to be aware that other chest pathologies are likely to be as prevalent as, or more prevalent than, active tuberculosis. However, non--tuberculosis chest X-ray abnormalities detected during chest X-ray screening for tuberculosis remain poorly characterized in the sub-Saharan African setting, with only minimal literature.

CASE PRESENTATION

In this case series, we report on four cases with non-tuberculosis abnormalities detected on CXR in TB TRIAGE + ACCURACY (ClinicalTrials.gov Identifier: NCT04666311), a study in adult presumptive tuberculosis cases at health facilities in Lesotho and South Africa to determine the diagnostic accuracy of two potential tuberculosis triage tests: computer-aided detection (CAD4TB v7, Delft, the Netherlands) and C-reactive protein (Alere Afinion, USA). The four Black African participants presented with the following chest X-ray abnormalities: a 59-year-old woman with pulmonary arteriovenous malformation, a 28-year-old man with pneumothorax, a 20-year-old man with massive bronchiectasis, and a 47-year-old woman with aspergilloma.

CONCLUSIONS

Solely using chest X-ray computer-aided detection systems based on artificial intelligence as a tuberculosis screening strategy in sub-Saharan Africa comes with benefits, but also risks. Due to the limitation of CAD4TB for non-tuberculosis-abnormality identification, the computer-aided detection software may miss significant chest X-ray abnormalities that require treatment, as exemplified in our four cases. Increased data collection, characterization of non-tuberculosis anomalies and research on the implications of these diseases for individuals and health systems in sub-Saharan Africa is needed to help improve existing artificial intelligence software programs and their use in countries with high tuberculosis burden.

Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications

Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists. In thoracic imaging, multiple applications are being evaluated for chest radiography and computed tomography and include applications for lung nodule evaluation and cancer imaging, quantifying diffuse lung disorders, and cardiac imaging, to name a few. This review aims to provide an overview of current clinical AI models, focusing on the most common clinical applications of AI in cardiothoracic imaging.

AI-Aided Disease Prediction in Visualized Medicine

Artificial intelligence (AI) is playing a vitally important role in promoting the revolution of future technology. Healthcare is one of the promising applications in AI, which covers medical imaging, diagnosis, robotics, disease prediction, pharmacy, health management, and hospital management. Numbers of achievements that made in these fields overturn every aspect in traditional healthcare system. Therefore, to understand the state-of-art AI in healthcare, as well as the chances and obstacles in its development, the applications of AI in disease detection and outlook and the future trends of AI-aided disease prediction were discussed in this chapter.

Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis

Tuberculosis (TB) remains one of the leading causes of death among infectious diseases worldwide. Early screening and diagnosis of pulmonary tuberculosis (PTB) is crucial in TB control, and tend to benefit from artificial intelligence. Here, we aimed to evaluate the diagnostic efficacy of a variety of artificial intelligence methods in medical imaging for PTB. We searched MEDLINE and Embase with the OVID platform to identify trials published update to November 2022 that evaluated the effectiveness of artificial-intelligence-based software in medical imaging of patients with PTB. After data extraction, the quality of studies was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). Pooled sensitivity and specificity were estimated using a bivariate random-effects model. In total, 3987 references were initially identified and 61 studies were finally included, covering a wide range of 124,959 individuals. The pooled sensitivity and the specificity were 91% (95% confidence interval (CI), 89-93%) and 65% (54-75%), respectively, in clinical trials, and 94% (89-96%) and 95% (91-97%), respectively, in model-development studies. These findings have demonstrated that artificial-intelligence-based software could serve as an accurate tool to diagnose PTB in medical imaging. However, standardized reporting guidance regarding AI-specific trials and multicenter clinical trials is urgently needed to truly transform this cutting-edge technology into clinical practice.

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model

Tuberculosis (TB) is an airborne disease caused by Mycobacterium tuberculosis. It is imperative to detect cases of TB as early as possible because if left untreated, there is a 70% chance of a patient dying within 10 years. The necessity for supplementary tools has increased in mid to low-income countries due to the rise of automation in healthcare sectors. The already limited resources are being heavily allocated towards controlling other dangerous diseases. Modern digital radiography (DR) machines, used for screening chest X-rays of potential TB victims are very practical. Coupled with computer-aided detection (CAD) with the aid of artificial intelligence, radiologists working in this field can really help potential patients. In this study, progressive resizing is introduced for training models to perform automatic inference of TB using chest X-ray images. ImageNet fine-tuned Normalization-Free Networks (NFNets) are trained for classification and the Score-Cam algorithm is utilized to highlight the regions in the chest X-Rays for detailed inference on the diagnosis. The proposed method is engineered to provide accurate diagnostics for both binary and multiclass classification. The models trained with this method have achieved 96.91% accuracy, 99.38% AUC, 91.81% sensitivity, and 98.42% specificity on a multiclass classification dataset. Moreover, models have also achieved top-1 inference metrics of 96% accuracy and 98% AUC for binary classification. The results obtained demonstrate that the proposed method can be used as a secondary decision tool in a clinical setting for assisting radiologists.

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.

Pulmonary tuberculosis diagnosis, differentiation and disease management: A review of radiomics applications

Pulmonary tuberculosis is a worldwide epidemic that can only be fought effectively with early and accurate diagnosis and proper disease management. The means of diagnosis and disease management should be easily accessible, cost effective and be readily available in the high tuberculosis burdened countries where it is most needed. Fortunately, the fast development of computer science in recent years has ensured that medical images can accurately be quantified. Radiomics is one such tool that can be used to quantify medical images. This review article focuses on the literature currently available on the application of radiomics explicitly for the purpose of diagnosis, differentiation from other pulmonary diseases and disease management of pulmonary tuberculosis. Despite using a formal search strategy, only five articles could be found on the application of radiomics to pulmonary tuberculosis. In all five articles reviewed, radiomic feature extraction was successfully used to quantify digital medical images for the purpose of comparing, or differentiating, pulmonary tuberculosis from other pulmonary diseases. This demonstrates that the use of radiomics for the purpose of tuberculosis disease management and diagnosis remains a valuable data mining opportunity not yet realised.

Deep learning applied to automatic disease detection using chest X-rays

Deep learning (DL) has shown rapid advancement and considerable promise when applied to the automatic detection of diseases using CXRs. This is important given the widespread use of CXRs across the world in diagnosing significant pathologies, and the lack of trained radiologists to report them. This review article introduces the basic concepts of DL as applied to CXR image analysis including basic deep neural network (DNN) structure, the use of transfer learning and the application of data augmentation. It then reviews the current literature on how DNN models have been applied to the detection of common CXR abnormalities (e.g. lung nodules, pneumonia, tuberculosis and pneumothorax) over the last few years. This includes DL approaches employed for the classification of multiple different diseases (multi-class classification). Performance of different techniques and models and their comparison with human observers are presented. Some of the challenges facing DNN models, including their future implementation and relationships to radiologists, are also discussed.

Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa

Computer-aided digital chest radiograph interpretation (CAD) can facilitate high-throughput screening for tuberculosis (TB), but its use in population-based active case-finding programs has been limited. In an HIV-endemic area in rural South Africa, we used a CAD algorithm (CAD4TBv5) to interpret digital chest x-rays (CXR) as part of a mobile health screening effort. Participants with TB symptoms or CAD4TBv5 score above the triaging threshold were referred for microbiological sputum assessment. During an initial pilot phase, a low CAD4TBv5 triaging threshold of 25 was selected to maximize TB case finding. We report the performance of CAD4TBv5 in screening 9,914 participants, 99 (1.0%) of whom were found to have microbiologically proven TB. CAD4TBv5 was able to identify TB cases at the same sensitivity but lower specificity as a blinded radiologist, whereas the next generation of the algorithm (CAD4TBv6) achieved comparable sensitivity and specificity to the radiologist. The CXRs of people with microbiologically confirmed TB spanned a range of lung field abnormality, including 19 (19.2%) cases deemed normal by the radiologist. HIV serostatus did not impact CAD4TB's performance. Notably, 78.8% of the TB cases identified during this population-based survey were asymptomatic and therefore triaged for sputum collection on the basis of CAD4TBv5 score alone. While CAD4TBv6 has the potential to replace radiologists for triaging CXRs in TB prevalence surveys, population-specific piloting is necessary to set the appropriate triaging thresholds. Further work on image analysis strategies is needed to identify radiologically subtle active TB.

Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis From Chest X-Ray Images: Data Mining Study

Background

Tuberculosis (TB) is one of the most infectious diseases that can be fatal. Its early diagnosis and treatment can significantly reduce the mortality rate. In the literature, several computer-aided diagnosis (CAD) tools have been proposed for the efficient diagnosis of TB from chest radiograph (CXR) images. However, the majority of previous studies adopted conventional handcrafted feature-based algorithms. In addition, some recent CAD tools utilized the strength of deep learning methods to further enhance diagnostic performance. Nevertheless, all these existing methods can only classify a given CXR image into binary class (either TB positive or TB negative) without providing further descriptive information.

Objective

The main objective of this study is to propose a comprehensive CAD framework for the effective diagnosis of TB by providing visual as well as descriptive information from the previous patients’ database.

Methods

To accomplish our objective, first we propose a fusion-based deep classification network for the CAD decision that exhibits promising performance over the various state-of-the-art methods. Furthermore, a multilevel similarity measure algorithm is devised based on multiscale information fusion to retrieve the best-matched cases from the previous database.

Results

The performance of the framework was evaluated based on 2 well-known CXR data sets made available by the US National Library of Medicine and the National Institutes of Health. Our classification model exhibited the best diagnostic performance (0.929, 0.937, 0.921, 0.928, and 0.965 for F1 score, average precision, average recall, accuracy, and area under the curve, respectively) and outperforms the performance of various state-of-the-art methods.

Conclusions

This paper presents a comprehensive CAD framework to diagnose TB from CXR images by retrieving the relevant cases and their clinical observations from the previous patients’ database. These retrieval results assist the radiologist in making an effective diagnostic decision related to the current medical condition of a patient. Moreover, the retrieval results can facilitate the radiologists in subjectively validating the CAD decision.

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.

Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

Image processing technologies and computer-aided diagnosis are medical technologies used to support decision-making processes of radiologists and medical professionals who provide treatment for lung disease. These methods involve using chest X-ray images to diagnose and detect lung lesions, but sometimes there are abnormal cases that take some time to occur. This experiment used 5810 images for training and validation with the MobileNet, Densenet-121 and Resnet-50 models, which are popular networks used to classify the accuracy of images, and utilized a rotational technique to adjust the lung disease dataset to support learning with these convolutional neural network models. The results of the convolutional neural network model evaluation showed that Densenet-121, with a state-of-the-art Mish activation function and Nadam-optimized performance. All the rates for accuracy, recall, precision and F1 measures totaled 98.88%. We then used this model to test 10% of the total images from the non-dataset training and validation. The accuracy rate was 98.97% for the result which provided significant components for the development of a computer-aided diagnosis system to yield the best performance for the detection of lung lesions.

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.

Developing and verifying automatic detection of active pulmonary tuberculosis from multi-slice spiral CT images based on deep learning

OBJECTIVE

Diagnosis of tuberculosis (TB) in multi-slice spiral computed tomography (CT) images is a difficult task in many TB prevalent locations in which experienced radiologists are lacking. To address this difficulty, we develop an automated detection system based on artificial intelligence (AI) in this study to simplify the diagnostic process of active tuberculosis (ATB) and improve the diagnostic accuracy using CT images.

DATA

A CT image dataset of 846 patients is retrospectively collected from a large teaching hospital. The gold standard for ATB patients is sputum smear, and the gold standard for normal and pneumonia patients is the CT report result. The dataset is divided into independent training and testing data subsets. The training data contains 337 ATB, 110 pneumonia, and 120 normal cases, while the testing data contains 139 ATB, 40 pneumonia, and 100 normal cases, respectively.

METHODS

A U-Net deep learning algorithm was applied for automatic detection and segmentation of ATB lesions. Image processing methods are then applied to CT layers diagnosed as ATB lesions by U-Net, which can detect potentially misdiagnosed layers, and can turn 2D ATB lesions into 3D lesions based on consecutive U-Net annotations. Finally, independent test data is used to evaluate the performance of the developed AI tool.

RESULTS

For an independent test, the AI tool yields an AUC value of 0.980. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value are 0.968, 0.964, 0.971, 0.971, and 0.964, respectively, which shows that the AI tool performs well for detection of ATB and differential diagnosis of non-ATB (i.e. pneumonia and normal cases).

CONCLUSION

An AI tool for automatic detection of ATB in chest CT is successfully developed in this study. The AI tool can accurately detect ATB patients, and distinguish between ATB and non- ATB cases, which simplifies the diagnosis process and lays a solid foundation for the next step of AI in CT diagnosis of ATB in clinical application.

Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks

Tuberculosis (TB) is an infectious disease that can lead towards death if left untreated. TB detection involves extraction of complex TB manifestation features such as lung cavity, air space consolidation, endobronchial spread, and pleural effusions from chest x-rays (CXRs). Deep learning based approach named convolutional neural network (CNN) has the ability to learn complex features from CXR images. The main problem is that CNN does not consider uncertainty to classify CXRs using softmax layer. It lacks in presenting the true probability of CXRs by differentiating confusing cases during TB detection. This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN). It deals with the uncertain cases that have low discernibility among the TB and non-TB manifested CXRs. The proposed TB identification methodology based on B-CNN is evaluated on two TB benchmark datasets, i.e., Montgomery and Shenzhen. For training and testing of proposed scheme we have utilized Google Colab platform which provides NVidia Tesla K80 with 12 GB of VRAM, single core of 2.3 GHz Xeon Processor, 12 GB RAM and 320 GB of disk. B-CNN achieves 96.42% and 86.46% accuracy on both dataset, respectively as compared to the state-of-the-art machine learning and CNN approaches. Moreover, B-CNN validates its results by filtering the CXRs as confusion cases where the variance of B-CNN predicted outputs is more than a certain threshold. Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty.

Artificial intelligence applications for thoracic imaging

Artificial intelligence is a hot topic in medical imaging. The development of deep learning methods and in particular the use of convolutional neural networks (CNNs), have led to substantial performance gain over the classic machine learning techniques. Multiple usages are currently being evaluated, especially for thoracic imaging, such as such as lung nodule evaluation, tuberculosis or pneumonia detection or quantification of diffuse lung diseases. Chest radiography is a near perfect domain for the development of deep learning algorithms for automatic interpretation, requiring large annotated datasets, in view of the high number of procedures and increasing data availability. Current algorithms are able to detect up to 14 common anomalies, when present as isolated findings. Chest computed tomography is another major field of application for artificial intelligence, especially in the perspective of large scale lung cancer screening. It is important for radiologists to apprehend, contribute actively and lead this new era of radiology powered by artificial intelligence. Such a perspective requires understanding new terms and concepts associated with machine learning. The objective of this paper is to provide useful definitions for understanding the methods used and their possibilities, and report current and future developments for thoracic imaging. Prospective validation of AI tools will be required before reaching routine clinical implementation.

Elucidation of Differential Nano-Textural Attributes for Normal Oral Mucosa and Pre-Cancer

Computational analysis on altered micro-nano-textural attributes of the oral mucosa may provide precise diagnostic information about oral potentially malignant disorders (OPMDs) instead of an existing handful of qualitative reports. This study evaluated micro-nano-textural features of oral epithelium from scanning electron microscopic (SEM) images and the sub-epithelial connective tissue from light microscopic (LM) and atomic force microscopic (AFM) images for normal and OPMD (namely oral sub-mucous fibrosis, i.e., OSF). Objective textural descriptors, namely discrete wavelet transform, gray-level co-occurrence matrix (GLCM), and local binary pattern (LBP), were extracted and fed to standard classifiers. Best classification accuracy of 87.28 and 93.21%; sensitivity of 93 and 96%; specificity of 80 and 91% were achieved, respectively, for SEM and AFM. In the study groups, SEM analysis showed a significant (p < 0.01) variation for all the considered textural descriptors, while for AFM, a remarkable alteration (p < 0.01) was only found in GLCM and LBP. Interestingly, sub-epithelial collagen nanoscale and microscale textural information from AFM and LM images, respectively, were complementary, namely microlevel contrast was more in normal (0.251) than OSF (0.193), while nanolevel contrast was more in OSF (0.283) than normal (0.204). This work, thus, illustrated differential micro-nano-textural attributes for oral epithelium and sub-epithelium to distinguish OPMD precisely and may be contributory in early cancer diagnostics.

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.

Computer-aided detection in chest radiography based on artificial intelligence: a survey

As the most common examination tool in medical practice, chest radiography has important clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease based on chest radiography has become one of the hot topics in medical imaging research. Based on the clinical applications, the study conducts a comprehensive survey on computer-aided detection (CAD) systems, and especially focuses on the artificial intelligence technology applied in chest radiography. The paper presents several common chest X-ray datasets and briefly introduces general image preprocessing procedures, such as contrast enhancement and segmentation, and bone suppression techniques that are applied to chest radiography. Then, the CAD system in the detection of specific disease (pulmonary nodules, tuberculosis, and interstitial lung diseases) and multiple diseases is described, focusing on the basic principles of the algorithm, the data used in the study, the evaluation measures, and the results. Finally, the paper summarizes the CAD system in chest radiography based on artificial intelligence and discusses the existing problems and trends.

Evaluation of the diagnostic accuracy of Computer-Aided Detection of tuberculosis on Chest radiography among private sector patients in Pakistan

The introduction of digital CXR with automated computer-aided interpretation, has given impetus to the role of CXR in TB screening, particularly in low resource, high-burden settings. The aim of this study was to evaluate the diagnostic accuracy of CAD4TB as a screening tool, implemented in the private sector in Karachi, Pakistan. This study analyzed retrospective data from CAD4TB and Xpert MTB/RIF testing carried out at two private TB treatment and diagnostic centers in Karachi. Sensitivity, specificity, potential Xperts saved, were computed and the receiver operator characteristic curves were constructed for four different models of CAD4TB. A total of 6,845 individuals with presumptive TB were enrolled in the study, 15.2% of which had MTB + ve result on Xpert. A high sensitivity (range 65.8-97.3%) and NPV (range 93.1-98.4%) were recorded for CAD4TB. The Area under the ROC curve (AUC) for CAD4TB was 0.79. CAD4TB with patient demographics (age and gender) gave an AUC of 0.83. CAD4TB offered high diagnostic accuracy. In low resource settings, CAD4TB, as a triage tool could minimize use of Xpert. Using CAD4TB in combination with age and gender data enhanced the performance of the software. Variations in demographic information generate different individual risk probabilities for the same CAD4TB scores.

Helicobacter Pylori infection detection from gastric X-ray images based on feature fusion and decision fusion

In this paper, a fully automatic method for detection of Helicobacter pylori (H. pylori) infection is presented with the aim of constructing a computer-aided diagnosis (CAD) system. In order to realize a CAD system with good performance for detection of H. pylori infection, we focus on the following characteristic of stomach X-ray examination. The accuracy of X-ray examination differs depending on the symptom of H. pylori infection that is focused on and the position from which X-ray images are taken. Therefore, doctors have to comprehensively assess the symptoms and positions. In order to introduce the idea of doctors' assessment into the CAD system, we newly propose a method for detection of H. pylori infection based on the combined use of feature fusion and decision fusion. As a feature fusion scheme, we adopt Multiple Kernel Learning (MKL). Since MKL can combine several features with determination of their weights, it can represent the differences in symptoms. By constructing an MKL classifier for each position, we can obtain several detection results. Furthermore, we introduce confidence-based decision fusion, which can consider the relationship between the classifier's performance and the detection results. Consequently, accurate detection of H. pylori infection becomes possible by the proposed method. Experimental results obtained by applying the proposed method to real X-ray images show that our method has good performance, close to the results of detection by specialists, and indicate that the realization of a CAD system for determining the risk of H. pylori infection is possible.

Automatic detection of pleural effusion in chest radiographs

Automated detection of Tuberculosis (TB) using chest radiographs (CXRs) is gaining popularity due to the lack of trained human readers in resource limited countries with a high TB burden. The majority of the computer-aided detection (CAD) systems for TB focus on detection of parenchymal abnormalities and ignore other important manifestations such as pleural effusion (PE). The costophrenic angle is a commonly used measure for detecting PE, but has limitations. In this work, an automatic method to detect PE in the left and right hemithoraces is proposed and evaluated on a database of 638 CXRs. We introduce a robust way to localize the costophrenic region using the chest wall contour as a landmark structure, in addition to the lung segmentation. Region descriptors are proposed based on intensity and morphology information in the region around the costophrenic recess. Random forest classifiers are trained to classify left and right hemithoraces. Performance of the PE detection system is evaluated in terms of recess localization accuracy and area under the receiver operating characteristic curve (AUC). The proposed method shows significant improvement in the AUC values as compared to systems which use lung segmentation and the costophrenic angle measurement alone.