Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model

Cardio-respiratory motion compensation for coronary roadmapping in fluoroscopic imaging

BACKGROUND

Inferring the shape and position of coronary artery poses challenges when using fluoroscopic image guidance during percutaneous coronary intervention (PCI) procedure. Although angiography enables coronary artery visualization, the use of injected contrast agent raises concerns about radiation exposure and the risk of contrast-induced nephropathy. To address these issues, dynamic coronary roadmapping overlaid on fluoroscopic images can provide coronary visual feedback without contrast injection.

PURPOSE

This paper proposes a novel cardio-respiratory motion compensation method that utilizes cardiac state synchronization and catheter motion estimation to achieve coronary roadmapping in fluoroscopic images.

METHODS

For more accurate cardiac state synchronization, video frame interpolation is applied to increase the frame rate of the original limited angiographic images, resulting in higher framerate and more adequate roadmaps. The proposed method also incorporates a multi-length cross-correlation based adaptive electrocardiogram (ECG) matching to address irregular cardiac motion situation. Furthermore, a shape-constrained path searching method is proposed to extract catheter structure from both fluoroscopic and angiographic image. Then catheter motion is estimated using a cascaded matching approach with an outlier removal strategy, leading to a final corrected roadmap.

RESULTS

Evaluation of the proposed method on clinical x-ray images demonstrates its effectiveness, achieving a 92.8% F1 score for catheter extraction on 589 fluoroscopic and angiographic images. Additionally, the method achieves a 5.6-pixel distance error of the coronary roadmap on 164 intraoperative fluoroscopic images.

CONCLUSIONS

Overall, the proposed method achieves accurate coronary roadmapping in fluoroscopic images and shows potential to overlay accurate coronary roadmap on fluoroscopic image in assisting PCI.

A Comparison of In Vitro Measurement and Ultrasound for Peripherally Inserted Central Catheter Placement in Premature Infants: A Before-and-After Self-Controlled Prospective Study

Background This study aimed to investigate the effectiveness of ultrasonography (US) and in vitro measurement (IVM) methods in localizing peripherally inserted central catheters (PICCs) in premature infants and analyze the relevant factors affecting the accuracy of IVM. Methodology The study employs a prospective before-and-after self-controlled clinical trial design. A total of 210 premature infants who underwent PICC catheterization were compared. We assessed the rate of catheter tip placement, consistency, and stability and analyzed the relevant factors. Results The study enrolled a total of 202 premature infants after eight infants dropped out. The one-time positioning rates of the PICC catheter tip using US and IVM were 100% and 73.8%, respectively. Concerning IVM, 53 (26.2%) patients did not reach the optimal position, with 24 (11.8%) patients having a shallow position and 29 (14.3%) having a deep position. The consistency of the two methods was 0.782 (p < 0.05). The degree of dispersion of US was 0.2 (0.0-0.4) cm, which was significantly smaller than IVM at 1.5 (0.0-1.8) cm. Gestational age less than 32 weeks (odds ratio (OR) = 6.64, 95% confidence interval (CI) = 1.43-30.81), weight less than 1,500 g (OR = 5.85, 95% CI = 2.11-16.20), body length less than 40 cm (OR = 15.36, 95% CI = 4.47-52.72), mechanical ventilation (OR = 5.13, 95% CI = 1.77-14.83), abdominal distension (OR = 78.18, 95% CI = 10.62-575.22), and bloating (OR = 8.81, 95% CI = 1.42-47.00) were risk factors that affected the accuracy of IVM. Conclusions Gestational age, weight, length, mechanical ventilation, abdominal distension, and swelling can lead to deviations with IVM. US can directly view the tip of the catheter, which is more accurate. Additionally, it is recommended to reduce the length of the catheter by 1.3 cm when using IVM to achieve the best-estimated placement length.

Effect of intracavitary electrocardiographic localization on the success rate and complications of PICC in infants

BACKGROUND

Peripherally inserted central catheter (PICC) is widely used in chemotherapy of children with malignant tumors because of its safe operation and long indwelling time.

OBJECTIVE

To investigate the effect of intracavitary electroencephalogram (CEEG) localization technique on the success rate and complications of PICC in infants.

METHODS

A total of 180 children with PICC catheterization and maintenance at Shijiazhuang People's Hospital First Hospital from January 2017 to January 2020 were selected and divided into control group (n= 90 cases) and observation group (n= 90 cases). The control group observed the tip position of the fixed catheter through X-ray film and adjusted the catheter until its tip was located in the superior vena cava. The observation group used intracavitary electrocardiogram positioning technology. Comparison of the effects of two groups on the success rate and complications of PICC puncture in infants and young children.

RESULTS

The success rate of one puncture in the observation group was significantly higher than that in the control group (P< 0.05). Within one month of catheterization, 13 cases had complications, with an incidence rate of 16.00% lower than the control group's 34.00% (27/80) (P< 0.05). The screening test results showed that the specificity, sensitivity, Youden index, accuracy, kappa coefficient, positive and negative predictive value were 88.89%, 97.56%, 0.86, 96.00%, 0.86, 0.86, respectively. The measured values were 97.56% and 88.89% respectively, and the cost and time of localization were lower than those of X-ray.

CONCLUSION

The technique of intracavitary electrogram can be more accurate for infants to place the tip of central venous catheter through peripheral vein, which can effectively improve the success rate of one puncture with low cost, and has high reliability, accuracy and practicability, which is safe and effective.

Automated precision localization of peripherally inserted central catheter tip through model-agnostic multi-stage networks

BACKGROUND

Peripherally inserted central catheters (PICCs) have been widely used as one of the representative central venous lines (CVCs) due to their long-term intravascular access with low infectivity. However, PICCs have a fatal drawback of a high frequency of tip mispositions, increasing the risk of puncture, embolism, and complications such as cardiac arrhythmias. To automatically and precisely detect it, various attempts have been made by using the latest deep learning (DL) technologies. However, even with these approaches, it is still practically difficult to determine the tip location because the multiple fragments phenomenon (MFP) occurs in the process of predicting and extracting the PICC line required before predicting the tip.

OBJECTIVE

This study aimed to develop a system generally applied to existing models and to restore the PICC line more exactly by removing the MFs of the model output, thereby precisely localizing the actual tip position for detecting its misposition.

METHODS

To achieve this, we proposed a multi-stage DL-based framework post-processing the PICC line extraction result of the existing technology. Our method consists of the following three stages: 1. Existing PICC line segmentation network for a baseline, 2. Patch-based PICC line refinement network, 3. PICC line reconnection network. The proposed second and third-stage models address MFs caused by the sparseness of the PICC line and the line disconnection due to confusion with anatomical structures respectively, thereby enhancing tip detection.

RESULTS

To verify the objective performance of the proposed MFCN, internal validation and external validation were conducted. For internal validation, learning (130 samples) and verification (150 samples) were performed with 280 data, including PICC among Chest X-ray (CXR) images taken at our institution. External validation was conducted using a public dataset called the Royal Australian and New Zealand College of Radiologists (RANZCR), and training (130 samples) and validation (150 samples) were performed with 280 data of CXR images, including PICC, which has the same number as that for internal validation. The performance was compared by root mean squared error (RMSE) and the ratio of single fragment images (RatioSFI) (i.e., the rate at which model predicts PICC as multiple sub-lines) according to whether or not MFCN is applied to seven conventional models (i.e., FCDN, UNET, AUNET, TUNET, FCDN-HT, UNET-ELL, and UNET-RPN). In internal validation, when MFCN was applied to the existing single model, MFP was improved by an average of 45 %. The RMSE improved over 63% from an average of 27.54 mm (17.16 to 35.80 mm) to 9.77 mm (9.11 to 10.98 mm). In external validation, when MFCN was applied, the MFP incidence rate decreased by an average of 32% and the RMSE decreased by an average of 65%. Therefore, by applying the proposed MFCN, we observed the consistent detection performance improvement of PICC tip location compared to the existing model.

CONCLUSION

In this study, we applied the proposed technique to the existing technique and demonstrated that it provides high tip detection performance, proving its high versatility and superiority. Therefore, we believe, in countries and regions where radiologists are scarce, that the proposed DL approach will be able to effectively detect PICC misposition on behalf of radiologists.

MWG-UNet: Hybrid Deep Learning Framework for Lung Fields and Heart Segmentation in Chest X-ray Images

Deep learning technology has achieved breakthrough research results in the fields of medical computer vision and image processing. Generative adversarial networks (GANs) have demonstrated a capacity for image generation and expression ability. This paper proposes a new method called MWG-UNet (multiple tasking Wasserstein generative adversarial network U-shape network) as a lung field and heart segmentation model, which takes advantages of the attention mechanism to enhance the segmentation accuracy of the generator so as to improve the performance. In particular, the Dice similarity, precision, and F1 score of the proposed method outperform other models, reaching 95.28%, 96.41%, and 95.90%, respectively, and the specificity surpasses the sub-optimal models by 0.28%, 0.90%, 0.24%, and 0.90%. However, the value of the IoU is inferior to the optimal model by 0.69%. The results show the proposed method has considerable ability in lung field segmentation. Our multi-organ segmentation results for the heart achieve Dice similarity and IoU values of 71.16% and 74.56%. The segmentation results on lung fields achieve Dice similarity and IoU values of 85.18% and 81.36%.

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.

Multi-task deep learning for medical image computing and analysis: A review

The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at least two tasks has attracted research attention. MTDL is a joint learning paradigm that harnesses the inherent correlation of multiple related tasks to achieve reciprocal benefits in improving performance, enhancing generalizability, and reducing the overall computational cost. This review focuses on the advanced applications of MTDL for medical image computing and analysis. We first summarize four popular MTDL network architectures (i.e., cascaded, parallel, interacted, and hybrid). Then, we review the representative MTDL-based networks for eight application areas, including the brain, eye, chest, cardiac, abdomen, musculoskeletal, pathology, and other human body regions. While MTDL-based medical image processing has been flourishing and demonstrating outstanding performance in many tasks, in the meanwhile, there are performance gaps in some tasks, and accordingly we perceive the open challenges and the perspective trends. For instance, in the 2018 Ischemic Stroke Lesion Segmentation challenge, the reported top dice score of 0.51 and top recall of 0.55 achieved by the cascaded MTDL model indicate further research efforts in high demand to escalate the performance of current models.

AI-based radiodiagnosis using chest X-rays: A review

Chest Radiograph or Chest X-ray (CXR) is a common, fast, non-invasive, relatively cheap radiological examination method in medical sciences. CXRs can aid in diagnosing many lung ailments such as Pneumonia, Tuberculosis, Pneumoconiosis, COVID-19, and lung cancer. Apart from other radiological examinations, every year, 2 billion CXRs are performed worldwide. However, the availability of the workforce to handle this amount of workload in hospitals is cumbersome, particularly in developing and low-income nations. Recent advances in AI, particularly in computer vision, have drawn attention to solving challenging medical image analysis problems. Healthcare is one of the areas where AI/ML-based assistive screening/diagnostic aid can play a crucial part in social welfare. However, it faces multiple challenges, such as small sample space, data privacy, poor quality samples, adversarial attacks and most importantly, the model interpretability for reliability on machine intelligence. This paper provides a structured review of the CXR-based analysis for different tasks, lung diseases and, in particular, the challenges faced by AI/ML-based systems for diagnosis. Further, we provide an overview of existing datasets, evaluation metrics for different[][15mm][0mm]Q5 tasks and patents issued. We also present key challenges and open problems in this research domain.

A deep learning model based on fusion images of chest radiography and X-ray sponge images supports human visual characteristics of retained surgical items detection

PURPOSE

Although a novel deep learning software was proposed using post-processed images obtained by the fusion between X-ray images of normal post-operative radiography and surgical sponge, the association of the retained surgical item detectability with human visual evaluation has not been sufficiently examined. In this study, we investigated the association of retained surgical item detectability between deep learning and human subjective evaluation.

METHODS

A deep learning model was constructed from 2987 training images and 1298 validation images, which were obtained from post-processing of the image fusion between X-ray images of normal post-operative radiography and surgical sponge. Then, another 800 images were used, i.e., 400 with and 400 without surgical sponge. The detection characteristics of retained sponges between the model and a general observer with 10-year clinical experience were analyzed using the receiver operator characteristics.

RESULTS

The following values from the deep learning model and observer were, respectively, derived: Cutoff values of probability were 0.37 and 0.45; areas under the curves were 0.87 and 0.76; sensitivity values were 85% and 61%; and specificity values were 73% and 92%.

CONCLUSION

For the detection of surgical sponges, we concluded that the deep learning model has higher sensitivity, while the human observer has higher specificity. These characteristics indicate that the deep learning system that is complementary to humans could support the clinical workflow in operation rooms for prevention of retained surgical items.

Classification of Central Venous Catheter Tip Position on Chest X-ray Using Artificial Intelligence

Recent studies utilizing deep convolutional neural networks (CNN) have described the central venous catheter (CVC) on chest radiography images. However, there have been no studies for the classification of the CVC tip position with a definite criterion on the chest radiograph. This study aimed to develop an algorithm for the automatic classification of proper depth with the application of automatic segmentation of the trachea and the CVC on chest radiographs using a deep CNN. This was a retrospective study that used plain chest supine anteroposterior radiographs. The trachea and CVC were segmented on images and three labels (shallow, proper, and deep position) were assigned based on the vertical distance between the tracheal carina and CVC tip. We used a two-stage approach model for the automatic segmentation of the trachea and CVC with U-net⁺⁺ and automatic classification of CVC placement with EfficientNet B4. The primary outcome was a successful three-label classification through five-fold validations with segmented images and a test with segmentation-free images. Of a total of 808 images, 207 images were manually segmented and the overall accuracy of the five-fold validation for the classification of three-class labels (mean (SD)) of five-fold validation was 0.76 (0.03). In the test for classification with 601 segmentation-free images, the average accuracy, precision, recall, and F1-score were 0.82, 0.73, 0.73, and 0.73, respectively. We achieved the highest accuracy value of 0.91 in the shallow position label, while the highest F1-score was 0.82 in the deep position label. A deep CNN can achieve a comparative performance in the classification of the CVC position based on the distance from the carina to the CVC tip as well as automatic segmentation of the trachea and CVC on plain chest radiographs.

Transfer-Learning-Based Approach for the Diagnosis of Lung Diseases from Chest X-ray Images

Using chest X-ray images is one of the least expensive and easiest ways to diagnose patients who suffer from lung diseases such as pneumonia and bronchitis. Inspired by existing work, a deep learning model is proposed to classify chest X-ray images into 14 lung-related pathological conditions. However, small datasets are not sufficient to train the deep learning model. Two methods were used to tackle this: (1) transfer learning based on two pretrained neural networks, DenseNet and ResNet, was employed; (2) data were preprocessed, including checking data leakage, handling class imbalance, and performing data augmentation, before feeding the neural network. The proposed model was evaluated according to the classification accuracy and receiver operating characteristic (ROC) curves, as well as visualized by class activation maps. DenseNet121 and ResNet50 were used in the simulations, and the results showed that the model trained by DenseNet121 had better accuracy than that trained by ResNet50.

Construction of a rabbit model with vinorelbine administration via peripherally inserted central catheter and dynamic monitoring of changes in phlebitis and thrombosis

Peripherally inserted central catheters (PICCs) are used for the administration of chemotherapy drugs, including vinorelbine. The present study aimed to construct a rabbit model with vinorelbine administration via PICC, and to dynamically monitor the formation of phlebitis and thrombosis. PICC was inserted into 48 rabbits following specific clinical procedures. The rabbits were randomly divided (n=6 per group) into the following eight groups: i) Control (PICC in place for 1 day); ii) 2nd day of PICC placement (received the first cycle of vinorelbine administration); iii) 3rd day of PICC placement; iv) 7th day of PICC placement; v) 14th day of PICC placement; vi) 21st day of PICC placement; vii) 23rd day of PICC placement (received the second cycle of vinorelbine administration); and viii) 24th day of PICC placement. Hematoxylin and eosin staining was performed on catheter, ear vein and anterior vena specimens. Prothrombin time was measured using an automatic coagulation analyzer, followed by routine blood tests. Serum levels of inflammation- and thrombosis-related factors, including C-reactive protein, D-dimer, interleukin-2, interleukin-6, P-selectin and E-selectin, were measured using ELISAs. X-ray examination confirmed that the rabbit model with vinorelbine administration via PICC was successfully constructed. On the 1st and 23rd day of PICC placement, thrombosis was observed in the catheter. Furthermore, on the 1st day of PICC placement, thrombosis was clearly observed in the ear vein and anterior vena samples. After vinorelbine administration, phlebitis occurred in the ear vein and anterior vena cava samples. With increasing time after vinorelbine administration via PICC, thrombosis and phlebitis were notably ameliorated. Moreover, on the day of vinorelbine administration, prothrombin time was significantly decreased and the serum levels of inflammation- and thrombosis-related factors were significantly increased compared with previous days. Collectively, the present study observed the formation and specific evolution of phlebitis and venous thrombosis after vinorelbine administration, providing a reference for the early prediction, timely prevention and treatment of PICC-related chemotherapy complications.

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.

Deep learning for chest X-ray analysis: A survey

Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs published before March 2021, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Detailed descriptions of all publicly available datasets are included and commercial systems in the field are described. A comprehensive discussion of the current state of the art is provided, including caveats on the use of public datasets, the requirements of clinically useful systems and gaps in the current literature.