Evaluation of a short training course of chest X-ray interpretation for the diagnosis of paediatric TB.

BACKGROUND: Chest X-ray (CXR) interpretation is challenging for the diagnosis of paediatric TB. We assessed the performance of a three half-day CXR training module for healthcare workers (HCWs) at low healthcare levels in six high TB incidence countries. METHODS: Within the TB-Speed Decentralization Study, we developed a three half-day training course to identify normal CXR, CXR of good quality and identify six TB-suggestive features. We performed a pre-post training assessment on a pre-defined set of 20 CXR readings. We compared the proportion of correctly interpreted CXRs and the median reading score before and after the training using the McNemar test and a linear mixed model. RESULTS: Of 191 HCWs, 43 (23%) were physicians, 103 (54%) nurses, 18 (9.4%) radiology technicians and 12 (6.3%) other professionals. Of 2,840 CXRs with both assessment, respectively 1,843 (64.9%) and 2,277 (80.2%) were correctly interpreted during pre-training and post-training (P < 0.001). The median reading score improved significantly from 13/20 to 16/20 after the training, after adjusting by country, facility and profession (adjusted ß = 3.31, 95% CI 2.44-4.47). CONCLUSION: Despite some limitations of the course assessment that did not include abnormal non-TB suggestive CXR, study findings suggest that a short CXR training course could improve HCWs' interpretation skills in diagnosing paediatric TB.

CONTEXTE: L'interprétation de la radiographie thoracique (CXR) est un défi pour le diagnostic de la TB pédiatrique. Nous avons évalué la performance d'un module de formation de trois demi-journées sur la CXR destiné aux agents de santé (HCWs) dans six pays où l'incidence de la TB est élevée et où les ressources en services de santé sont limitées. MÉTHODES: Dans le cadre de l'étude de décentralisation TB-Speed, nous avons mis au point un cours de formation de trois demi-journées pour identifier une CXR normale, une CXR de bonne qualité et six caractéristiques suggestives de la TB. Nous avons effectué une évaluation avant et après la formation sur un ensemble prédéfini de 20 clichés radiologiques. Nous avons comparé la proportion de CXR correctement interprétées et le score médian de lecture avant et après la formation à l'aide du test de McNemar et d'un modèle linéaire mixte. RÉSULTATS: Sur les 191 HCWs, 43 (23%) étaient des médecins, 103 (54%) des infirmières, 18 (9,4%) des techniciens en radiologie et 12 (6,3%) d'autres professionnels. Sur 2 840 CXR avec les deux évaluations, respectivement 1 843 (64,9%) et 2 277 (80,2%) ont été correctement interprétées avant et après la formation (P < 0,001). Le score médian de lecture s'est amélioré de manière significative, passant de 13/20 à 16/20 après la formation, après ajustement par pays, établissement et profession (ß ajusté = 3,31; IC 95% 2,44­4,47). CONCLUSION: Malgré certaines limites de l'évaluation du cours qui n'incluait pas de CXR anormale non évocatrice de TB, les résultats de l'étude suggèrent qu'une formation courte sur la CXR pourrait améliorer les compétences d'interprétation des HCWs dans le diagnostic de la TB pédiatrique.

Artificial intelligence's contribution to early pulmonary lesion detection in chest X-rays: insights from two retrospective studies on a Czech population.

In recent years healthcare is undergoing significant changes due to technological innovations, with Artificial Intelligence (AI) being a key trend. Particularly in radiodiagnostics, according to studies, AI has the potential to enhance accuracy and efficiency. We focus on AI's role in diagnosing pulmonary lesions, which could indicate lung cancer, based on chest X-rays. Despite lower sensitivity in comparison to other methods like chest CT, due to its routine use, X-rays often provide the first detection of lung lesions. We present our deep learning-based solution aimed at improving lung lesion detection, especially during early-stage of illness. We then share results from our previous studies validating this model in two different clinical settings: a general hospital with low prevalence findings and a specialized oncology center. Based on a quantitative comparison with the conclusions of radiologists of different levels of experience, our model achieves high sensitivity, but lower specificity than comparing radiologists. In the context of clinical requirements and AI-assisted diagnostics, the experience and clinical reasoning of the doctor play a crucial role, therefore we currently lean more towards models with higher sensitivity over specificity. Even unlikely suspicions are presented to the doctor. Based on these results, it can be expected that in the future artificial intelligence will play a key role in the field of radiology as a supporting tool for evaluating specialists. To achieve this, it is necessary to solve not only technical but also medical and regulatory aspects. It is crucial to have access to quality and reliable information not only about the benefits but also about the limitations of machine learning and AI in medicine.

Deep Learning-Based Localization and Detection of Malpositioned Nasogastric Tubes on Portable Supine Chest X-Rays in Intensive Care and Emergency Medicine: A Multi-center Retrospective Study.

Malposition of a nasogastric tube (NGT) can lead to severe complications. We aimed to develop a computer-aided detection (CAD) system to localize NGTs and detect NGT malposition on portable chest X-rays (CXRs). A total of 7378 portable CXRs were retrospectively retrieved from two hospitals between 2015 and 2020. All CXRs were annotated with pixel-level labels for NGT localization and image-level labels for NGT presence and malposition. In the CAD system, DeepLabv3 + with backbone ResNeSt50 and DenseNet121 served as the model architecture for segmentation and classification models, respectively. The CAD system was tested on images from chronologically different datasets (National Taiwan University Hospital (National Taiwan University Hospital)-20), geographically different datasets (National Taiwan University Hospital-Yunlin Branch (YB)), and the public CLiP dataset. For the segmentation model, the Dice coefficients indicated accurate delineation of the NGT course (National Taiwan University Hospital-20: 0.665, 95% confidence interval (CI) 0.630-0.696; National Taiwan University Hospital-Yunlin Branch: 0.646, 95% CI 0.614-0.678). The distance between the predicted and ground-truth NGT tips suggested accurate tip localization (National Taiwan University Hospital-20: 1.64 cm, 95% CI 0.99-2.41; National Taiwan University Hospital-Yunlin Branch: 2.83 cm, 95% CI 1.94-3.76). For the classification model, NGT presence was detected with high accuracy (area under the receiver operating characteristic curve (AUC): National Taiwan University Hospital-20: 0.998, 95% CI 0.995-1.000; National Taiwan University Hospital-Yunlin Branch: 0.998, 95% CI 0.995-1.000; CLiP dataset: 0.991, 95% CI 0.990-0.992). The CAD system also detected NGT malposition with high accuracy (AUC: National Taiwan University Hospital-20: 0.964, 95% CI 0.917-1.000; National Taiwan University Hospital-Yunlin Branch: 0.991, 95% CI 0.970-1.000) and detected abnormal nasoenteric tube positions with favorable performance (AUC: 0.839, 95% CI 0.807-0.869). The CAD system accurately localized NGTs and detected NGT malposition, demonstrating excellent potential for external generalizability.

Rare Central Venous Catheter Malpositions: A Case Series.

Central venous catheters are a procedure that provides vascular access, allowing the application of various clinical treatments and the measurement of some hemodynamic values. It provides access to the internal jugular vein, subclavian vein, and, femoral vein with a large-bore catheter. There are mechanical, infectious, and thromboembolic complications resulting from central venous catheter placement and care. Central venous catheter malposition is a rare catheter complication that may be encountered. The location of the central venous catheter can be evaluated with imaging techniques such as posteroanterior chest radiograph, ultrasonography, central venous catheter waveform, and transesophageal echocardiography. Five malposition cases detected by imaging after the central venous catheter procedure in our clinic are presented.

A Case of Postoperative Noncardiogenic Edema: Negative Pressure Pulmonary Edema.

A case of complete recovery of negative pressure pulmonary edema after a Cottle surgery in a 24-year-old male. Teaching point: Negative pressure pulmonary edema is an important cause of postoperative noncardiogenic edema, with the spontaneous disappearance of all complaints within a relatively short period.

Clinical Validation of a Deep Learning-Based Software for Lumbar Bone Mineral Density and T-Score Prediction from Chest X-ray Images.

Screening for osteoporosis is crucial for early detection and prevention, yet it faces challenges due to the low accuracy of calcaneal quantitative ultrasound (QUS) and limited access to dual-energy X-ray absorptiometry (DXA) scans. Recent advances in AI offer a promising solution through opportunistic screening using existing medical images. This study aims to utilize deep learning techniques to develop a model that analyzes chest X-ray (CXR) images for osteoporosis screening. This study included the AI model development stage and the clinical validation stage. In the AI model development stage, the combined dataset of 5122 paired CXR images and DXA reports from the patients aged 20 to 98 years at a medical center was collected. The images were enhanced and filtered for hardware retention such as pedicle screws, bone cement, artificial intervertebral discs or severe deformity in target level of T12 and L1. The dataset was then separated into training, validating, and testing datasets for model training and performance validation. In the clinical validation stage, we collected 440 paired CXR images and DXA reports from both the TCVGH and Joy Clinic, including 304 pared data from TCVGH and 136 paired data from Joy Clinic. The pre-clinical test yielded an area under the curve (AUC) of 0.940, while the clinical validation showed an AUC of 0.946. Pearson's correlation coefficient was 0.88. The model demonstrated an overall accuracy, sensitivity, and specificity of 89.0%, 88.7%, and 89.4%, respectively. This study proposes an AI model for opportunistic osteoporosis screening through CXR, demonstrating good performance and suggesting its potential for broad adoption in preliminary screening among high-risk populations.

CSAMDT: Conditional Self Attention Memory-Driven Transformers for Radiology Report Generation from Chest X-Ray.

A radiology report plays a crucial role in guiding patient treatment, but writing these reports is a time-consuming task that demands a radiologist's expertise. In response to this challenge, researchers in the subfields of artificial intelligence for healthcare have explored techniques for automatically interpreting radiographic images and generating free-text reports, while much of the research on medical report creation has focused on image captioning methods without adequately addressing particular report aspects. This study introduces a Conditional Self Attention Memory-Driven Transformer model for generating radiological reports. The model operates in two phases: initially, a multi-label classification model, utilizing ResNet152 v2 as an encoder, is employed for feature extraction and multiple disease diagnosis. In the second phase, the Conditional Self Attention Memory-Driven Transformer serves as a decoder, utilizing self-attention memory-driven transformers to generate text reports. Comprehensive experimentation was conducted to compare existing and proposed techniques based on Bilingual Evaluation Understudy (BLEU) scores ranging from 1 to 4. The model outperforms the other state-of-the-art techniques by increasing the BLEU 1 (0.475), BLEU 2 (0.358), BLEU 3 (0.229), and BLEU 4 (0.165) respectively. This study's findings can alleviate radiologists' workloads and enhance clinical workflows by introducing an autonomous radiological report generation system.

Acute Coronary Syndrome and Acute Abdomen Suspected for Type B Aortic Dissection in an Elderly Woman.

Being an uncommon and challenging disorder, acute aortic dissection (AAD) can have fatal outcomes in the event of missed diagnosis or treatment delay. AAD could easily be misdiagnosed, as symptoms usually mimic other common clinical syndromes showing up in Accident and Emergency (A&E), including acute coronary syndrome (ACS), pericarditis, pulmonary embolism, acute abdomen, musculoskeletal pain, as well as presenting as heart failure, stroke, syncope, and absent peripheral pulses. We present a case of a 77-year-old female who presented to the medical decision unit with acute-onset chest, back, and abdominal pain that occurred on standing for six hours She was thought initially to have acute coronary syndrome based on electrocardiography (ECG) changes, troponin, a normal chest X-ray, and no blood pressure discrepancies in upper extremities. Due to worsening abdominal pain and a previous history of a perforated diverticulum, contrast computed tomography (CT) of the abdomen was arranged and this showed acute type B aortic dissection. By the time the CT was performed, the patient had been in hospital for 16 hours, almost 22 hours from the onset of pain.

AMFP-net: Adaptive multi-scale feature pyramid network for diagnosis of pneumoconiosis from chest X-ray images.

Early detection of pneumoconiosis by routine health screening of workers in the mining industry is critical for preventing the progression of this incurable disease. Automated pneumoconiosis classification in chest X-ray images is challenging due to the low contrast of opacities, inter-class similarity, intra-class variation and the existence of artifacts. Compared to traditional methods, convolutional neural networks have shown significant improvement in pneumoconiosis classification tasks, however, accurate classification remains challenging due to mainly the inability to focus on semantically meaningful lesion opacities. Most existing networks focus on high level abstract information and ignore low level detailed object information. Different from natural images where an object occupies large space, the classification of pneumoconiosis depends on the density of small opacities inside the lung. To address this issue, we propose a novel two-stage adaptive multi-scale feature pyramid network called AMFP-Net for the diagnosis of pneumoconiosis from chest X-rays. The proposed model consists of 1) an adaptive multi-scale context block to extract rich contextual and discriminative information and 2) a weighted feature fusion module to effectively combine low level detailed and high level global semantic information. This two-stage network first segments the lungs to focus more on relevant regions by excluding irrelevant parts of the image, and then utilises the segmented lungs to classify pneumoconiosis into different categories. Extensive experiments on public and private datasets demonstrate that the proposed approach can outperform state-of-the-art methods for both segmentation and classification.

The role of chest X-rays when screening for latent tuberculosis infection in patients with inflammatory bowel disease before starting biologic treatment.

BACKGROUND: Guidelines generally recommend a combination of immunological assays and chest X-ray imaging (CXR) when screening for latent tuberculosis infection (LTBI) prior to biologic treatment in inflammatory bowel disease (IBD). OBJECTIVE: To investigate whether CXR identify patients with suspected LTBI/TB who were not identified with QuantiFERON tests (QFT) when screening for LTBI/TB before starting biologic treatment in IBD patients. METHODS: Single-center, retrospective cohort study of patients with inflammatory bowel disease who had a QFT and a CXR prior to initiation of biologic treatment in a 5-year period (October 1st, 2017 to September 30th, 2022). RESULTS: 520 patients (56% female, mean age 40.1 years) were included. The majority had none or few risk factors for TB (as reflected by the demographic characteristics) but some risk factors for having false negative QFT results (concurrent glucocorticoid treatment and inflammatory activity). QFT results were positive in 8 patients (1.5%), inconclusive in 18 (3.5%) and negative in 494 (95.0%). Only 1 patient (0.19%) had CXR findings suspicious of LTBI. This patient also had a positive QFT and was subsequently diagnosed with active TB. All patients with negative or inconclusive QFT had CXR without any findings suggesting LTBI/TB. One patient developed active TB after having initiated biologic treatment in spite of having negative QFT and a normal CXR at screening. CONCLUSION: In a population with low risk of TB, the benefits of supplementing the QFT with a CXR are limited and are unlikely to outweigh the cost in both patient test-burden, radioactive exposure, and economic resources.

A deep learning-based algorithm for pulmonary tuberculosis detection in chest radiography.

In tuberculosis (TB), chest radiography (CXR) patterns are highly variable, mimicking pneumonia and many other diseases. This study aims to evaluate the efficacy of Google teachable machine, a deep neural network-based image classification tool, to develop algorithm for predicting TB probability of CXRs. The training dataset included 348 TB CXRs and 3806 normal CXRs for training TB detection. We also collected 1150 abnormal CXRs and 627 normal CXRs for training abnormality detection. For external validation, we collected 250 CXRs from our hospital. We also compared the accuracy of the algorithm to five pulmonologists and radiological reports. In external validation, the AI algorithm showed areas under the curve (AUC) of 0.951 and 0.975 in validation dataset 1 and 2. The accuracy of the pulmonologists on validation dataset 2 showed AUC range of 0.936-0.995. When abnormal CXRs other than TB were added, AUC decreased in both human readers (0.843-0.888) and AI algorithm (0.828). When combine human readers with AI algorithm, the AUC further increased to 0.862-0.885. The TB CXR AI algorithm developed by using Google teachable machine in this study is effective, with the accuracy close to experienced clinical physicians, and may be helpful for detecting tuberculosis by CXR.

Deep Learning-based Lung dose Prediction Using Chest X-ray Images in Non-small Cell Lung Cancer Radiotherapy.

Purpose: This study aimed to develop a deep learning model for the prediction of V20 (the volume of the lung parenchyma that received ≥20 Gy) during intensity-modulated radiation therapy using chest X-ray images. Methods: The study utilized 91 chest X-ray images of patients with lung cancer acquired routinely during the admission workup. The prescription dose for the planning target volume was 60 Gy in 30 fractions. A convolutional neural network-based regression model was developed to predict V20. To evaluate model performance, the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) were calculated with conducting a four-fold cross-validation method. The patient characteristics of the eligible data were treatment period (2018-2022) and V20 (19.3%; 4.9%-30.7%). Results: The predictive results of the developed model for V20 were 0.16, 5.4%, and 4.5% for the R2, RMSE, and MAE, respectively. The median error was -1.8% (range, -13.0% to 9.2%). The Pearson correlation coefficient between the calculated and predicted V20 values was 0.40. As a binary classifier with V20 <20%, the model showed a sensitivity of 75.0%, specificity of 82.6%, diagnostic accuracy of 80.6%, and area under the receiver operator characteristic curve of 0.79. Conclusions: The proposed deep learning chest X-ray model can predict V20 and play an important role in the early determination of patient treatment strategies.

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" - there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification.

Chest Radiograph Screening for Detecting Subclinical Tuberculosis in Asymptomatic Household Contacts, Peru.

The World Health Organization's end TB strategy promotes the use of symptom and chest radiograph screening for tuberculosis (TB) disease. However, asymptomatic early states of TB beyond latent TB infection and active disease can go unrecognized using current screening criteria. We conducted a longitudinal cohort study enrolling household contacts initially free of TB disease and followed them for the occurrence of incident TB over 1 year. Among 1,747 screened contacts, 27 (52%) of the 52 persons in whom TB subsequently developed during follow-up had a baseline abnormal radiograph. Of contacts without TB symptoms, persons with an abnormal radiograph were at higher risk for subsequent TB than persons with an unremarkable radiograph (adjusted hazard ratio 15.62 [95% CI 7.74-31.54]). In young adults, we found a strong linear relationship between radiograph severity and time to TB diagnosis. Our findings suggest chest radiograph screening can extend to detecting early TB states, thereby enabling timely intervention.

Lung segment anything model (LuSAM): a decoupled prompt-integrated framework for automated lung segmentation on chest x-Ray images.

Accurate lung segmentation in chest x-ray images plays a pivotal role in early disease detection and clinical decision-making. In this study, we introduce an innovative approach to enhance the precision of lung segmentation using the Segment Anything Model (SAM). Despite its versatility, SAM faces the challenge of prompt decoupling, often resulting in misclassifications, especially with intricate structures like the clavicle. Our research focuses on the integration of spatial attention mechanisms within SAM. This approach enables the model to concentrate specifically on the lung region, fostering adaptability to image variations and reducing the likelihood of false positives. This work has the potential to significantly advance lung segmentation, improving the identification and quantification of lung anomalies across diverse clinical contexts.

Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images.

We present a deep learning (DL) network-based approach for detecting and semantically segmenting two specific types of tuberculosis (TB) lesions in chest X-ray (CXR) images. In the proposed method, we use a basic U-Net model and its enhanced versions to detect, classify, and segment TB lesions in CXR images. The model architectures used in this study are U-Net, Attention U-Net, U-Net++, Attention U-Net++, and pyramid spatial pooling (PSP) Attention U-Net++, which are optimized and compared based on the test results of each model to find the best parameters. Finally, we use four ensemble approaches which combine the top five models to further improve lesion classification and segmentation results. In the training stage, we use data augmentation and preprocessing methods to increase the number and strength of lesion features in CXR images, respectively. Our dataset consists of 110 training, 14 validation, and 98 test images. The experimental results show that the proposed ensemble model achieves a maximum mean intersection-over-union (MIoU) of 0.70, a mean precision rate of 0.88, a mean recall rate of 0.75, a mean F1-score of 0.81, and an accuracy of 1.0, which are all better than those of only using a single-network model. The proposed method can be used by clinicians as a diagnostic tool assisting in the examination of TB lesions in CXR images.

Are routine chest radiographs still indicated after central line insertion? A scoping review.

Introduction: Central venous catheters are increasingly inserted using point-of-care ultrasound (POCUS) guidance. Following insertion, it is still common to request a confirmatory chest radiograph for subclavian and internal jugular lines, at least outside of the operating theater. This scoping review addresses: (i) the justification for routine post-insertion radiographs, (ii) whether it would better to use post-insertion POCUS instead, and (iii) the perceived barriers to change. Methods: We searched the electronic databases, Ovid MEDLINE (1946-) and Ovid EMBASE (1974-), using the MESH terms ("Echography" OR "Ultrasonography" OR "Ultrasound") AND "Central Venous Catheter" up until February 2023. We also searched clinical practice guidelines, and targeted literature, including cited and citing articles. We included adults (⩾18 years) and English and French language publications. We included randomized control trials, prospective and retrospective cohort studies, systematic reviews, and surveys. Results: Four thousand seventy-one articles were screened, 117 full-text articles accessed, and 41 retained. Thirteen examined cardiac/vascular methods; 5 examined isolated contrast-enhanced ultrasonography; 7 examined isolated rapid atrial swirl sign; and 13 examined combined/integrated methods. In addition, three systematic reviews/meta-analyses and one survey addressed barriers to POCUS adoption. Discussion: We believe that the literature supports retiring the routine post-central line chest radiograph. This is not only because POCUS has made line insertion safer, but because POCUS performs at least as well, and is associated with less radiation, lower cost, time savings, and greater accuracy. There has been less written about perceived barriers to change, but the literature shows that these concerns- which include upfront costs, time-to-train, medicolegal concerns and habit- can be challenged and hence overcome.

Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease Detection.

Accurately identifying and locating lesions in chest X-rays has the potential to significantly enhance diagnostic efficiency, quality, and interpretability. However, current methods primarily focus on detecting of specific diseases in chest X-rays, disregarding the presence of multiple diseases in a single chest X-ray scan. Moreover, the diversity in lesion locations and attributes introduces complexity in accurately discerning specific traits for each lesion, leading to diminished accuracy when detecting multiple diseases. To address these issues, we propose a novel detection framework that enhances multi-scale lesion feature extraction and fusion, improving lesion position perception and subsequently boosting chest multi-disease detection performance. Initially, we construct a multi-scale lesion feature extraction network to tackle the uniqueness of various lesion features and locations, strengthening the global semantic correlation between lesion features and their positions. Following this, we introduce an instance-aware semantic enhancement network that dynamically amalgamates instance-specific features with high-level semantic representations across various scales. This adaptive integration effectively mitigates the loss of detailed information within lesion regions. Additionally, we perform lesion region feature mapping using candidate boxes to preserve crucial positional information, enhancing the accuracy of chest disease detection across multiple scales. Experimental results on the VinDr-CXR dataset reveal a 6% increment in mean average precision (mAP) and an 8.4% improvement in mean recall (mR) when compared to state-of-the-art baselines. This demonstrates the effectiveness of the model in accurately detecting multiple chest diseases by capturing specific features and location information.