Image similarity-based cardiac rhythm device identification from X-rays using feature point matching

Chest X-ray Foreign Objects Detection Using Artificial Intelligence

Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis.

Content-based image retrieval for the diagnosis of myocardial perfusion imaging using a deep convolutional autoencoder

BACKGROUND

Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) plays a crucial role in the optimal treatment strategy for patients with coronary heart disease. We tested the feasibility of feature extraction from MPI using a deep convolutional autoencoder (CAE) model.

METHODS

Eight hundred and forty-three pairs of stress and rest myocardial perfusion images were collected from consecutive patients who underwent cardiac scintigraphy in our hospital between December 2019 and February 2022. We trained a CAE model to reproduce the input paired image data, so as the encoder to output a 256-dimensional feature vector. The extracted feature vectors were further dimensionally reduced via principal component analysis (PCA) for data visualization. Content-based image retrieval (CBIR) was performed based on the cosine similarity of the feature vectors between the query and reference images. The agreement of the radiologist's finding between the query and retrieved MPI was evaluated using binary accuracy, precision, recall, and F1-score.

RESULTS

A three-dimensional scatter plot with PCA revealed that feature vectors retained clinical information such as percent summed difference score, presence of ischemia, and the location of scar reported by radiologists. When CBIR was used as a similarity-based diagnostic tool, the binary accuracy was 81.0%.

CONCLUSION

The results indicated the utility of unsupervised feature learning for CBIR in MPI.

Pro-Con Debate: Are Patients With a Cardiovascular Implantable Electronic Device Suitable to Receive Care in a Free-Standing Ambulatory Surgery Center?

Migration of surgical and other procedures that require anesthesia care from a hospital to a free-standing ambulatory surgery center (ASC) continues to grow. Patients with cardiac implantable electronic devices (CIED) might benefit from receiving their care in a free-standing ASC setting. However, these patients have cardiovascular comorbidities that can elevate the risk of major adverse cardiovascular events. CIEDs are also complex devices and perioperative management varies between devices marketed by various manufacturers and require consultation and ancillary services, which may not be available in a free-standing ASC. Thus, perioperative care of these patients can be challenging. Therefore, the suitability of this patient population in a free-standing ASC remains highly controversial. Although applicable advisories exist, considerable discussion continues with surgeons and other proceduralists about the concerns of anesthesiologists. In this Pro-Con commentary article, we discuss the arguments for and against scheduling a patient with a CIED in a free-standing ASC.

Three-Dimensional Reconstruction of Pacemaker Lead Trajectory From Orthogonal Chest X-Rays: A Proof of Concept

Understanding the lead trajectory is important in preventing complications after cardiac rhythm device implantation. In this report, we sought to reconstruct the three-dimensional (3D) shape of a pacing lead from radiographs taken at 90-degree angles. All image data were obtained from a 65-year-old male patient, who underwent pacemaker implantation at our hospital due to third-degree atrioventricular block in 2016. Both frontal and lateral chest X-rays were taken just after the device implantation (supine position) and on the post-procedural day 1 (upright position), respectively. Fluorine-18-fluorodeoxyglucose positron emission tomography/CT was performed 75 days after the pacemaker implantation for the diagnosis of cardiac sarcoidosis. Contours of the ventricular leads were manually traced in each X-ray image and saved as Scalable Vector Format (SVG) files using the GNU Image Manipulation Program (GIMP). The 3D reconstruction was performed on Blender 2.93, which is an open-source computer graphics software. The lead trajectory could be reconstructed from bidirectional radiographs, which may allow for further investigation of the 3D shape change of the pacemaker leads.