Chest X-ray Interpretation Research Articles

Clinicians' Interpretation of Unreported Chest Radiographs in Biologic Prescription Workup Service: A Comprehensive Analysis.

Clinicians without a radiology specializationface difficulties when they attempt to interpret chest X-rays (CXRs), a crucial and extensively utilized diagnostic tool that plays a fundamental role in the detection of pulmonary and cardiovascular disorders. This cross-sectional study assessed the confidence and competence of clinicians, including junior specialty trainees, higher specialty trainees, and specialist nurses, in interpreting CXRs before starting biological treatment. An online survey was used to collect data from clinicians in various healthcare settings, focusing on their experience, training, confidence levels, and CXR interpretation proficiency. The survey uncovered clinicians' insufficient confidence in interpreting the pre-biological screening CXRs despite their clinical expertise. This uncertainty raises concerns about potential misinterpretations, affecting timely treatment decisions. A Kruskal-Wallis test indicated a significant difference between training levels required with a p-value of 0.001, rejecting the null hypothesis. Subsequently, a Dunn-Bonferroni test revealed that the higher specialty trainee-specialist nurse pair differed significantly, with the specialist nurse group requiring more training. This study highlighted the need for enhanced radiology education for clinicians involved in chest radiograph interpretation for pre-biological screening. Implementing a structured training program is essential to improve skills and ensure accurate interpretation of non-formally reported chest radiographs, ultimately enhancing patient outcomes and healthcare practices.

Evaluation of lung ultrasound in the diagnosis of respiratory distress in neonates

Background: Respiratory distress (RD) is a leading cause of morbidity and mortality among neonates in neonatal intensive care units (NICUs). Traditionally, clinical criteria combined with chest X-ray have been considered the gold standard for diagnosing respiratory distress. This study aims to assess the efficiency of lung ultrasound in diagnosing respiratory distress, with a focus on preventing neonates from unnecessary radiation exposure. Methods: A multicentric prospective study was performed where neonates with gestational age 28-40 weeks were included based on presence of clinical signs of respiratory distress. Neonates with congenital anomalies or those who received surfactant therapy were excluded. Within 6 hrs of birth, these neonates underwent X ray chest and ultrasound. Final interpretation of chest x-ray was done by the blinded radiologist. A trained neonatologist used transthoracic view in ultrasound for the initial interpretation. Results: A total of 80 neonates were enrolled in the study, among whom 49 were diagnosed with respiratory distress syndrome, 22 with transient tachypnoea of the newborn, 4 with pneumonia, and 5 classified as normal. Lung ultrasound exhibited a sensitivity of 95.9% and a specificity of 90.3% for the diagnosis of respiratory distress, with a positive predictive value of 94% and a negative predictive value of 93.3%. Conclusions: Lung ultrasound proves to be a highly efficient diagnostic tool for respiratory distress in neonates, offering the added advantage of avoiding harmful radiation exposure associated with chest X-rays. Early detection of respiratory distress can facilitate timely management and significantly improve neonatal outcomes. Further adoption and validation of point of care lung ultrasound in clinical practice hold the potential to enhance neonatal care.

Acceptability of decentralizing childhood tuberculosis diagnosis in low-income countries with high tuberculosis incidence: Experiences and perceptions from health care workers in Sub-Saharan Africa and South-East Asia.

Decentralizing childhood tuberculosis services, including diagnosis, is now recommended by the WHO and could contribute to increasing tuberculosis detection in high burden countries. However, implementing microbiological tests and clinical evaluation could be challenging for health care workers (HCWs) in Primary Health Centers (PHCs) and even District Hospitals (DHs). We sought to assess the acceptability of decentralizing a comprehensive childhood tuberculosis diagnosis package from HCWs' perspective. We conducted implementation research nested within the TB-Speed Decentralization study. HCWs from two health districts of Cambodia, Cameroon, Côte d'Ivoire, Mozambique, Sierra Leone, and Uganda implemented systematic screening, nasopharyngeal aspirates (NPA) and stool sample collection with molecular testing, clinical evaluation and chest X-ray (CXR) interpretation. We investigated their experiences and perceptions in delivering the diagnostic package components in 2020-21 using individual semi-structured interviews. We conducted thematic analysis, supported by the Theoretical Framework of Acceptability. HCWs (n = 130, 55% female, median age 36 years, 53% nurses, 72% PHC-based) perceived that systematic screening, although increasing workload, was beneficial as it improved childhood tuberculosis awareness. Most HCWs shared satisfaction and confidence in performing NPA, despite procedure duration, need to involve parents/colleagues and discomfort for children. HCWs shared positive attitudes towards stool sample-collection but were frustrated by delayed stool collection associated with cultural practices, transport and distance challenges. Molecular testing, conducted by nurses or laboratory technicians, was perceived as providing quality results, contributing to diagnosis. Clinical evaluation and diagnosis raised self-efficacy issues and need for continuous training and clinical mentoring. HCWs valued CXR, however complained that technical and logistical problems limited access to digital reports. Referral from PHC to DH was experienced as burdensome. HCWs at DH and PHC-levels perceived and experienced decentralized childhood tuberculosis diagnosis as acceptable. Implementation however could be hampered by feasibility issues, and calls for innovative referral mechanisms for patients, samples and CXR.

AI-clinician collaboration via disagreement prediction: A decision pipeline and retrospective analysis of real-world radiologist-AI interactions

Clinical decision support tools can improve diagnostic performance or reduce variability, but they are also subject to post-deployment underperformance. Although using AI in an assistive setting offsets many concerns with autonomous AI in medicine, systems that present all predictions equivalently fail to protect against key AI safety concerns. We design a decision pipeline that supports the diagnostic model with an ecosystem of models, integrating disagreement prediction, clinical significance categorization, and prediction quality modeling to guide prediction presentation. We characterize disagreement using data from a deployed chest X-ray interpretation aid and compare clinician burden in this proposed pipeline to the diagnostic model in isolation. The average disagreement rate is 6.5%, and the expected burden reduction is 4.8%, even if 5% of disagreements on urgent findings receive a second read. We conclude that, in our production setting, we can adequately balance risk mitigation with clinician burden if disagreement false positives are reduced.

The diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF).

Typical signs of heart failure (HF), like increased cardiothoracic ratio (CTR) and pleural effusion, can be seen on X-ray. Artificial Intelligence (AI) can help in the early and quicker diagnosis of HF. The study's goal was to demonstrate that the AI interpretation of chest X-rays can assist the clinician in diagnosing HF. Patients older than 45 years were included in the study. The study analyzed 10 100 deidentified outpatient chest X-rays by AI algorithm. The AI-generated report was later verified by an independent radiologist. Patients with CTR > 0.5 and pleural effusion were marked as potential HF. Flagged patients underwent confirmatory tests, and those labeled as negative also underwent further investigations to rule out HF. Out of 10 100, the AI algorithm detected 183 (1.8%) patients with increased CTR and pleural effusion on chest X-rays. One hundred and six out of 183 underwent diagnostic tests. Eighty-two (77%) out of 106 were diagnosed with HF according to current guidelines. From the remaining 9917 patients, 106 patients were randomly selected. Nine (8%) out of them were diagnosed with HF. The positive predictive value of AI for diagnosing HF is 77%, and the negative predictive value is 91%. More than half (54.9%) of newly diagnosed patients had HF with preserved ejection fraction. HF is a risky condition with nonspecific symptoms that are difficult to diagnose, especially in the early stages. Using AI assistance for X-ray interpretation can be helpful for early diagnosis of HF especially HF with preserved ejection fraction.

The Effects of Various Teaching Methods on Chest X-ray Interpretation Skills Among Medical Students and Interns: A Systematic Review.

Chest X-ray (CXR) is a common tool used in medical practice. Medical students and interns should acquire knowledge of CXR interpretation, as it is an essential diagnostic tool for a large spectrum of diseases. This systematic review aimed to compare the effect of different intervention techniques on the competency of medical students and interns to demonstrate the level of confidence and competence in interpreting common presentations of CXRs. The population, intervention, comparison, and outcomes (PICO) framework was used to formulate the review question. All related articles in five databases (PubMed, Web of Science, Scopus, Medline, and Embase) were retrieved and the search was completed in March 2023with no limiters on date and time. The number of relevant studies was 469. A multi-level approach through the Rayyan platform was used for the screening and exclusion processes. Eleven articles were included in the systematic review consisting of eight randomized controlled trials, one quasi-experimental study, one cross-sectional study, and one interventional cohort. Results showed significant effects of teaching methods utilizing deductive or inductive approach, clinical history, patient care comfort survey, and SAFMEDS (Say-All-Fast-Minute-Every-Day-Shuffled). Contrarily, no significant effect was shown by flipped classroom models and mixed and blocked practice, peer-assisted learning vs. expert-assisted learning, and Chester, an artificial intelligence tool. This review identified beneficial approaches that may enhance the learning outcomes of interpreting CXRs for medical students and interns, highlighting the remarkable impact of SAFMEDSon medical students' ability to identify CXR findings as well as the availability and practicality of online and e-learning resources for students.

Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation.

To assess whether a computer-aided detection (CADe) system could serve as a learning tool for radiology residents in chest X-ray (CXR) interpretation. Eight radiology residents were asked to interpret 500 CXRs for the detection of five abnormalities, namely pneumothorax, pleural effusion, alveolar syndrome, lung nodule, and mediastinal mass. After interpreting 150 CXRs, the residents were divided into 2 groups of equivalent performance and experience. Subsequently, group 1 interpreted 200 CXRs from the "intervention dataset" using a CADe as a second reader, while group 2 served as a control by interpreting the same CXRs without the use of CADe. Finally, the 2 groups interpreted another 150 CXRs without the use of CADe. The sensitivity, specificity, and accuracy before, during, and after the intervention were compared. Before the intervention, the median individual sensitivity, specificity, and accuracy of the eight radiology residents were 43% (range: 35-57%), 90% (range: 82-96%), and 81% (range: 76-84%), respectively. With the use of CADe, residents from group 1 had a significantly higher overall sensitivity (53% [n = 431/816] vs 43% [n = 349/816], p < 0.001), specificity (94% [i = 3206/3428] vs 90% [n = 3127/3477], p < 0.001), and accuracy (86% [n = 3637/4244] vs 81% [n = 3476/4293], p < 0.001), compared to the control group. After the intervention, there were no significant differences between group 1 and group 2 regarding the overall sensitivity (44% [n = 309/696] vs 46% [n = 317/696], p = 0.666), specificity (90% [n = 2294/2541] vs 90% [n = 2285/2542], p = 0.642), or accuracy (80% [n = 2603/3237] vs 80% [n = 2602/3238], p = 0.955). Although it improves radiology residents' performances for interpreting CXRs, a CADe system alone did not appear to be an effective learning tool and should not replace teaching. Although the use of artificial intelligence improves radiology residents' performance in chest X-rays interpretation, artificial intelligence cannot be used alone as a learning tool and should not replace dedicated teaching. • With CADe as a second reader, residents had a significantly higher sensitivity (53% vs 43%, p < 0.001), specificity (94% vs 90%, p < 0.001), and accuracy (86% vs 81%, p < 0.001), compared to residents without CADe. • After removing access to the CADe system, residents' sensitivity (44% vs 46%, p = 0.666), specificity (90% vs 90%, p = 0.642), and accuracy (80% vs 80%, p = 0.955) returned to that of the level for the group without CADe.

Strategies to increase childhood tuberculosis case detection at the primary health care level: Lessons from an active case finding study in Zambia.

In high TB burden settings, it is estimated that 10-20% of total notifications should be children, however, currently only 6-8% of the total TB notifications in Zambia are children. We assessed whether the implementation of a multicomponent strategy, at primary healthcare facilities, that systematically targets barriers at each step of the childhood TB diagnostic cascade can increase childhood TB case detection. We conducted a controlled, interrupted time series analysis to compare childhood TB case notifications before (January 2018-December 2019), and during implementation (January 2020-September 2021) in two intervention and two control Level 1 hospitals in Lusaka, Zambia. At each of the intervention facilities, we implemented a multicomponent strategy constituting: (1) capacity development on childhood TB and interpretation of chest x-ray, (2) TB awareness-raising and demand creation activities, (3) setting up fast track TB services, (4) strengthening of household contact tracing, and (5) improving access to digital chest X-ray for TB screening and Xpert MTB/Rif Ultra for TB diagnosis, through strengthening sample collection in children. Among 5,150 children < 15 years screened at the two intervention facilities during the study period, 503 (9.8% yield) were diagnosed with TB. Of these, 433 (86.1%) were identified through facility-based activities (10.5% yield) and 70 (13.9%) were identified through household contact tracing (6.9% yield). Overall, 446 children (88.7%) children with TB were clinically diagnosed. Following implementation of the multicomponent strategy, the proportion children contributed to total TB notifications immediately changed by +1.5% (95%CI: -3.5, 6.6) and -4.4% (95%CI: -7.5, 1.4) at the intervention and control sites, respectively (difference 6.0% [95%CI: -0.7, 12.7]), p = 0.08); the proportion of childhood notifications increased 0.9% (95%CI: -0.7, 2.5%) each quarter at the intervention sites relative to pre-implementation trends, while declining 1.2% (-95%CI: -1.8, -0.6) at the control sites (difference 2.1% [95%CI: 0.1, 4.2] per quarter between, p = 0.046); this translated into 352 additional and 85 fewer childhood TB notifications at the intervention and control sites, respectively, compared to the pre-implementation period. A standardized package of strategies to improve childhood TB detection at primary healthcare facilities was feasible to implement and was associated with a sustained improvement in childhood TB notifications.

Image projective transformation rectification with synthetic data for smartphone-captured chest X-ray photos classification

Automatic interpretation of chest X-ray (CXR) photos taken by smartphones at the same performance level as with digital CXRs is challenging, due to the projective transformation caused by the non-ideal camera position. Existing rectification methods for other camera-captured photos (document photos, license plate photos, etc.) cannot precisely rectify the projective transformation of CXR photos, due to its specific projective transformation type. In this paper, we propose an innovative deep learning-based Projective Transformation Rectification Network (PTRN) to automatically rectify the projective transformation of CXR photos by predicting the projective transformation matrix. Additionally, synthetic CXR photos are generated for training with the consideration of visual artifacts of natural images. The effectiveness of the proposed classification pipeline with PTRN is evaluated in the CheXphoto smartphone-captured CXR photo classification competition. It achieves first place with a huge performance improvement (ours 0.850, second-best 0.762, in AUC). Moreover, experimental results show that our approach successfully achieves the same performance level of digital CXR classification (AUC 0.893) on CXR photo classification (AUC 0.893).

Care-Seeking Behavior of Families of North Indian Children Suffering From WHO-Defined Severe Community-Acquired Pneumonia: A Hospital-Based Prospective Study.

Background Community-acquired pneumonia (CAP) is one of the leading causes of deathin children under five. In developing countries, delayed treatment seeking has been associated with mortality and morbidity. There are only a few studies in India evaluating care-seeking behavior, particularly in children with CAP. Methods The present study was a hospital-basedprospective semi-qualitative study. The study was conducted on parents or caregivers of consecutively hospitalized children under five (two to 59 months) with WHO-defined severe CAP along with radiological abnormalities consistent with CAP.Categorization of CAP and interpretation of chest X-rays (CXR) were done as per WHO criteria. Complicated CAP was categorized as severe pneumonia and had additional characteristics, including pleural effusion/empyema/pneumothorax requiring intercostal drainage, acute respiratory distress syndrome, or septic shock. Results After the screening of 420 consecutively hospitalized children under fivewith WHO-defined severe CAP along with radiological abnormalities consistent with it, 350 children were recruited in the present study. Among the recruited children, 58.6% experienced delayed care seeking, and among delayed care seekers, 94.6% presented with complications or developed complications during their hospital stay. The median delay in medical attention was three days. It also found that mothers with education levels below a high school had delayed care-seeking behavior. Mothers noticed the illness first in the majority of subjects (190, 54.3%), followed by fathers (78, 22.3%). Visiting traditional healers (46, 22.4%) and opting for home-bound remedies (44, 21.5%) were among the most common reasons for delayed care seeking. Fast breathing was the most concerning symptomamong the parents and caregivers of the hospitalized children due to severe CAP followed by retractions, cough,and drowsiness. Retractions, drowsiness, and inability to feed were significantly recognized as alarming symptoms by the parents and caregivers in children with complicated CAP. Delayed care-seeking behavior was more prevalent in families from rural areas than in urban areas. If decision takers were in closerelation with the sick child, chances of delayed care were less and vice versa. In urban areas, mothers can make decisions in significantly higher numbers than in rural areas, while grandmothers were more involved in decision-making in rural areas. Conclusion The delayed care-seeking behavior was significantly higher in children with complicated CAP. Delayed care-seeking behavior was more prevalent in families from rural areas than in urban areas. The most common reasons for delayed care-seeking behavior were home remedies and visiting traditional healers.Caregivers needto be more aware of the danger signs of CAP and the consequences of treatment delay.

Artificial Intelligence-Based Software with CE Mark for Chest X-ray Interpretation: Opportunities and Challenges.

Chest X-ray (CXR) is the most important technique for performing chest imaging, despite its well-known limitations in terms of scope and sensitivity. These intrinsic limitations of CXR have prompted the development of several artificial intelligence (AI)-based software packages dedicated to CXR interpretation. The online database "AI for radiology" was queried to identify CE-marked AI-based software available for CXR interpretation. The returned studies were divided according to the targeted disease. AI-powered computer-aided detection software is already widely adopted in screening and triage for pulmonary tuberculosis, especially in countries with few resources and suffering from high a burden of this disease. AI-based software has also been demonstrated to be valuable for the detection of lung nodules detection, automated flagging of positive cases, and post-processing through the development of digital bone suppression software able to produce digital bone suppressed images. Finally, the majority of available CE-marked software packages for CXR are designed to recognize several findings, with potential differences in sensitivity and specificity for each of the recognized findings.

An AI tool for chest X-ray interpretation and lung cancer pre-screening in developing countries.

1548 Background: The current lung cancer screening strategy has not had a major impact on public health, facing many challenges during its implementation in developing countries. Although chest radiography (CXR) is not the study of choice for screening; it is one of the most accessible studies used at all levels of care, so implementing an AI tool capable of CXR interpretation as well as lung cancer risk assessment on a large scale could improve the selection of patients for lung cancer screening. The aim of our study was to develop and evaluate this tool. Methods: The dataset used for this tool development was obtained from the NIH "ChestX-ray14” public database. The set was divided into 3 subgroups for training, testing, and validation in a ratio of 80/10/10% respectively. We performed a structuration of metadata and an exploratory analysis as well as preprocessing of the images to standardize their size and resolution before the training. A model based on the adaptation of the momentum for learning was used as an optimizer of the CNN algorithm used. Additionally, a questionnaire based on the PLCO clinical criteria was included for the identification of patients at high risk of developing lung cancer. Results: Standard performance metrics were evaluated as shown. Conclusions: Our model is comparable to certified radiologists in accuracy at identifying patterns of a CXR. The use of clinical patient information improves the pre-selection of patients with a high risk of developing lung cancer. In developing countries, the implementation of this tool on a large scale will allow more patients to be screened and early diagnosed.[Table: see text]

A Case of Missed Golden Sign: A Case Report

Introduction: Chest X-rays have always been the baseline investigation to go for in the evaluation of pulmonary diseases. With the advent of new imaging techniques, focus has shifted from X-rays to computed tomography chest with a lesser time spent on the interpretation of chest X-ray. In spite of this, chest X-ray remains an indispensable investigation as it is simple, easy, cheap, associated with low radiation dose, and most importantly, it gives significant information to guide further management. Methods: We present a case in which important finding in chest X-ray was missed and identified later which led to a complete shift in the line of treatment. Results: Chest X-ray is the basic investigation for respiratory disorders, and its correct interpretation is of utmost importance. Conclusion: Keeping a high index of suspicion for nontubercular pulmonary diseases even in younger population is the need of hour.

Comparison of chest X-ray interpretation by pediatric pulmonologists, pediatric radiologists, and pediatric residents in children with suspected foreign body aspiration-a retrospective cohort study.

CXR has a high positive predictive value and independently predicts FBA in children; however, normal CXR should not rule out FBA. Predictability is variable among different disciplines. • Chest X-ray is an important tool in the assessment of children with suspected foreign body aspiration (FBA). • Chest X-ray can be interpreted as normal in one-third of the cases. • Chest X-ray independently predicts FBA in children, with a high positive predictive value. • The ability of chest x-ray to predict FBA in children differs between pediatric residents, pediatric radiologists, and pediatric pulmonologists.

Assessing and comparing chest radiograph interpretation in the Department of Internal Medicine at the University of the Witwatersrand medical school, according to seniority.

Chest radiographs are a common diagnostic tool in the internal medicine department, and correct interpretation is imperative for adequate patient management. To determine the diagnostic accuracy of common pathologies in South Africa that are evident on chest radiographs, and to determine whether there are discrepancies according to different levels of qualification of doctors rotating through the internal medicine department, and which factors contribute to an accurate diagnosis. Fifteen chest radiographs with common pathologies were given to all doctors rotating through the Department of Internal Medicine at Chris Hani Baragwanath Academic Hospital, and they were asked to interpret them. Information pertaining to their experience, designation and confidence in chest radiograph interpretation was also obtained. Diagnostic accuracy according to years of experience was as follows: 0 - 5 years 27.0%, 6 - 10 years 43.0%, and >10 years 47.9%. For different designations, accuracy was as follows: consultants 50.5%, registrars 40.9%, medical officers 36.4%, and interns 19.5%. Participants who were confident obtained a mean score of 39.4% and those who were not, a mean score of 31.6%. Chest radiographs are readily accessible and used daily in clinical practice in numerous facilities. An accurate diagnosis is important to provide quality healthcare. Improved training in interpretation for all, but especially for junior doctors, should be a priority in our training facilities. What the study adds. This study tested the diagnostic accuracy with regard to common pathologies present on chest X ray by doctors rotating through, or stationed at the internal medicine department at an academic hospital. Implications of the findings. Interpretation of chest X-rays was generally poor but the study did find that this improves with experience and confidence in diagnostic ability. These findings are significant in that they indicate a need to implement improved teaching programs in radiological interpretation, especially at an undergraduate level.

A Web-Based Platform for the Automatic Stratification of ARDS Severity

Acute respiratory distress syndrome (ARDS), including severe pulmonary COVID infection, is associated with a high mortality rate. It is crucial to detect ARDS early, as a late diagnosis may lead to serious complications in treatment. One of the challenges in ARDS diagnosis is chest X-ray (CXR) interpretation. ARDS causes diffuse infiltrates through the lungs that must be identified using chest radiography. In this paper, we present a web-based platform leveraging artificial intelligence (AI) to automatically assess pediatric ARDS (PARDS) using CXR images. Our system computes a severity score to identify and grade ARDS in CXR images. Moreover, the platform provides an image highlighting the lung fields, which can be utilized for prospective AI-based systems. A deep learning (DL) approach is employed to analyze the input data. A novel DL model, named Dense-Ynet, is trained using a CXR dataset in which clinical specialists previously labelled the two halves (upper and lower) of each lung. The assessment results show that our platform achieves a recall rate of 95.25% and a precision of 88.02%. The web platform, named PARDS-CxR, assigns severity scores to input CXR images that are compatible with current definitions of ARDS and PARDS. Once it has undergone external validation, PARDS-CxR will serve as an essential component in a clinical AI framework for diagnosing ARDS.

Evaluación metodológica de las revisiones sistemáticas basadas en la utilización de sistemas de inteligencia artificial en radiografía de tórax

IntroductionIn recent years, systems that use artificial intelligence (AI) in medical imaging have been developed, such as the interpretation of chest X-ray to rule out pathology. This has produced an increase in systematic reviews (SR) published on this topic. This article aims to evaluate the methodological quality of SRs that use AI for the diagnosis of thoracic pathology by simple chest X-ray. Material and methodsSRs evaluating the use of AI systems for the automatic reading of chest X-ray were selected. Searches were conducted (from inception to May 2022): PubMed, EMBASE, and the Cochrane Database of Systematic Reviews. Two investigators selected the reviews. From each SR, general, methodological and transparency characteristics were extracted. The PRISMA statement for diagnostic tests (PRISMA-DTA) and AMSTAR-2 were used. A narrative synthesis of the evidence was performed. Protocol registry: Open Science Framework: https://osf.io/4b6u2/. ResultsAfter applying the inclusion and exclusion criteria, 7 SRs were selected (mean of 36 included studies per review). All the included SRs evaluated “deep learning” systems in which chest X-ray was used for the diagnosis of infectious diseases. Only 2 (29%) SRs indicated the existence of a review protocol. None of the SRs specified the design of the included studies or provided a list of excluded studies with their justification. Six (86%) SRs mentioned the use of PRISMA or one of its extensions. The risk of bias assessment was performed in 4 (57%) SRs. One (14%) SR included studies with some validation of AI techniques. Five (71%) SRs presented results in favour of the diagnostic capacity of the intervention. All SRs were rated “critically low” following AMSTAR-2 criteria. ConclusionsThe methodological quality of SRs that use AI systems in chest radiography can be improved. The lack of compliance in some items of the tools used means that the SRs published in this field must be interpreted with caution.

Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review

Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is necessary for clinicians as these tools begin to permeate practice. This systematic review aimed to provide an overview of machine learning applications designed to facilitate CXR interpretation. A systematic search strategy was executed to identify research into machine learning algorithms capable of detecting >2 radiographic findings on CXRs published between January 2020 and September 2022. Model details and study characteristics, including risk of bias and quality, were summarized. Initially, 2248 articles were retrieved, with 46 included in the final review. Published models demonstrated strong standalone performance and were typically as accurate, or more accurate, than radiologists or non-radiologist clinicians. Multiple studies demonstrated an improvement in the clinical finding classification performance of clinicians when models acted as a diagnostic assistance device. Device performance was compared with that of clinicians in 30% of studies, while effects on clinical perception and diagnosis were evaluated in 19%. Only one study was prospectively run. On average, 128,662 images were used to train and validate models. Most classified less than eight clinical findings, while the three most comprehensive models classified 54, 72, and 124 findings. This review suggests that machine learning devices designed to facilitate CXR interpretation perform strongly, improve the detection performance of clinicians, and improve the efficiency of radiology workflow. Several limitations were identified, and clinician involvement and expertise will be key to driving the safe implementation of quality CXR machine learning systems.

Chest X-ray Interpretation: Detecting Devices and Device-Related Complications.

This short review has the aim of helping the radiologist to identify medical devices when interpreting a chest X-ray, as well as looking for their most commonly detectable complications. Nowadays, many different medical devices are used, often together, especially in critical patients. It is important for the radiologist to know what to look for and to remember the technical factors that need to be considered when checking each device's positioning.

Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Computer-aided diagnosis (CAD) models exploit artificial intelligence (AI) for chest X-ray (CXR) examination to identify the presence of tuberculosis (TB) and can improve the feasibility and performance of CXR for TB screening and triage. At the same time, CXR interpretation is a time-consuming and subjective process. Furthermore, high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis. Therefore, computer-aided diagnosis (CAD) models using machine learning (ML) and deep learning (DL) can be designed for screening TB accurately. With this motivation, this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification (WSODTL-TBC) model on Chest X-rays (CXR). The presented WSODTL-TBC model aims to detect and classify TB on CXR images. Primarily, the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation. Besides, a pre-trained residual network with a two-dimensional convolutional neural network (2D-CNN) model is applied to extract feature vectors. In addition, the WSO algorithm with long short-term memory (LSTM) model was employed for identifying and classifying TB, where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology, showing the novelty of the work. The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset, and the outcomes were investigated in many aspects. The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.