Introduction The identification of perpendicular vascular changes of the vocal folds (PVC) has emerged as an indicator of malignancy in the endoscopic assessment of glottic lesions. Classifying them further into wide and narrow-angle PVC aids in differentiating between papillomas and carcinomas. Accurate identification requires clinical experience. Artificial Intelligence (AI) algorithms can support endoscopic evaluations.
This research protocol paper presents key lessons learned at Innolab IGT, a medical, technology, and innovation laboratory that has short distances between operating rooms and labs, responsive communication, and the ability to identify clinical needs directly.
Abstract Introduction : The grade of reperfusion after endovascular treatment of ischemic stroke e.g. mechanical thrombectomy is determined based on the mTICI score. The mTICI score shows significant interrater variability; it is usually biased towards better reperfusion results if selfassessed by the operator. We therefore developed a semiautomated image processing technique for assessing and evaluating the degree of reperfusion independently, resulting in a more objective mTICI score. Methods : Fifty angiography datasets of patients who were treated with mechanical thrombectomy for middle cerebral artery (MCA) occlusion were selected from our database. Image datasets were standardized by adjustment of field of view and orientation. Based on pixel intensity features, the internal carotid artery (ICA) curve was detected automatically and used as a starting point for identifying the target downstream territory (TDT) of the MCA on the DSA series. Furthermore, a grid with predefined dimensions was used to divide the TDT into checkzones and be classified as perfused or unperfused. Results: The algorithm detected the TDT and classified each zone of the grid as perfused or unperfused. Lastly, the percentage of the perfused area in the TDT was calculated for each patient and compared to the grading of experienced clinical users. Conclusion : A semi-automatic image-processing workflow was developed to evaluate perfusion rate based on angiographic images. The approach can be used for the objective calculation of the mTICI score. The semi-automatic grading is currently feasible for MCA occlusion but can be extended for other brain territories. The work shows a starting point for a machine learning approach to achieve a fully automated system that can evaluate and give an accurate mTICI score to become a common AI-based grading standard in the coming near future.
Abstract Purpose Percutaneous needle insertion is one of the most common minimally invasive procedures. The clinician’s experience and medical imaging support are essential to the procedure’s safety. However, imaging comes with inaccuracies due to artifacts, and therefore sensor-based solutions were proposed to improve accuracy. However, sensors are usually embedded in the needle tip, leading to design limitations. A novel concept was proposed for capturing tip–tissue interaction information through audio sensing, showing promising results for needle guidance. This work demonstrates that this audio approach can provide important puncture information by comparing audio and force signal dynamics during insertion. Methods An experimental setup for inserting a needle into soft tissue was prepared. Audio and force signals were synchronously recorded at four different insertion velocities, and a dataset of 200 recordings was acquired. Indicators related to different aspects of the force and audio were compared through signal-to-signal and event-to-event correlation analysis. Results High signal-to-signal correlations between force and audio indicators regardless of the insertion velocity were obtained. The force curvature indicator obtained the best correlation performances to audio with more than $$70\%$$ 70% of the correlations higher than 0.6. The event-to-event correlation analysis shows that a puncture event in the force is generally identifiable in audio and that their intensities firmly related. Conclusions Audio contains valuable information for monitoring needle tip/tissue interaction. Significant dynamics obtained from a well-known sensor as force can also be extracted from audio, regardless of insertion velocities.
SummaryMinimally invasive techniques using endoscopes for image-guided therapy are common in the surgical field and in internal medicine. Interventional procedures in the past were performed with either fluoroscopic, sono-graphic or CT-guidance, but now MRI-guided interventional procedures are being developed. Combining these technologies will improve surgical access and reduce complications. In today's minimally invasive therapy, tomography technology (CT, EBT, MRI) can be used for precise and transparent guidance of endoscopes and surgical instruments inside the body. This will offer a safe and effective access into the body, especially in high risk areas and lead to the new field of 'Surgical Tomography'.
Cerebrovascular diseases such as stenosis, atherosclerosis or distention of the carotid artery are accountable for about 1 million death per year across Europe.Diagnostic tools like ultrasound imaging, angiography or magnetic resonance based imaging require specific hardware and highly depend on the experience of the examining clinician.In contrast auscultation with a stethoscope can be used to screen for and subjectively quantify bruits -audible vascular sounds associated with turbulent blood flow in the arteries.Dynamical changes in the flow due to pathological narrowing of the vessel can indicate the need for additional diagnostic investigations.A reliable auscultation setup is prerequisite to ensure high signal quality, adequate processing and the objective evaluation of a still subjectively assessed audible signal.We propose a computer assisted auscultation device for the characterisation of carotid bruits to facilitate the objective assessment, screening and monitoring of long-term changes in the vessel condition.Acoustic signals are acquired using two integrated audio sensors in combination with a particular mechanical setup to ensure a reliable interface to the skin.Data are transferred to a mini computer for real-time visualisation and evaluation of sensor position and signal quality before recording starts.Main goal of this work are design considerations regarding the mechanical interface of the proposed system to the skin.An experimental setup was used to compare the signal quality and morphology of different setups to a commercially available digital stethoscope as reference system.A combined system with two different interface configurations is proposed.Current limitations of the system and potential improvements are discussed.
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