Body Fluid Collection Devices for Ostomy Patients: A Review.

Abdominal ostomy surgery has a severe impact on individuals' daily lives. These procedures are typically indicated for conditions such as cancer, inflammatory bowel disease, or traumatic injuries. They involve creating an artificial opening, denominated the stoma, in the abdominal area to divert feces or urine, establishing a connection between the affected organs and the body's exterior.

Deep-DM: Deep-driven deformable model for 3D image segmentation using limited data.

Objective - Medical image segmentation is essential for several clinical tasks, including diagnosis, surgical and treatment planning, and image-guided interventions. Deep Learning (DL) methods have become the state-of-the-art for several image segmentation scenarios. However, a large and well-annotated dataset is required to effectively train a DL model, which is usually difficult to obtain in clinical practice, especially for 3D images.

Robust 3D breast reconstruction based on monocular images and artificial intelligence for robotic guided oncological interventions.

Breast cancer is a global public health concern. For women with suspicious breast lesions, the current diagnosis requires a biopsy, which is usually guided by ultrasound (US). However, this process is challenging due to the low quality of the US image and the complexity of dealing with the US probe and the surgical needle simultaneously, making it largely reliant on the surgeon's expertise.

A Comparative Study of Deep Learning Methods for Multi-Class Semantic Segmentation of 2D Kidney Ultrasound Images.

Ultrasound (US) imaging is a widely used medical imaging modality for the diagnosis, monitoring, and surgical planning for kidney conditions. Thus, accurate segmentation of the kidney and internal structures in US images is essential for the assessment of kidney function and the detection of pathological conditions, such as cysts, tumors, and kidney stones. Therefore, there is a need for automated methods that can accurately segment the kidney and internal structures in US images.

Deep learning networks in the segmentation of the left atrial appendage in 2D ultrasound: A comparative analysis.

Left atrial appendage (LAA) is the major source of thromboembolism in patients with non-valvular atrial fibrillation. Currently, LAA occlusion can be offered as a treatment for these patients, obstructing the LAA through a percutaneously delivered device. Nevertheless, correct device sizing is a complex task, requiring manual analysis of medical images.

Deep Learning Networks for Breast Lesion Classification in Ultrasound Images: A Comparative Study.

Accurate lesion classification as benign or malignant in breast ultrasound (BUS) images is a critical task that requires experienced radiologists and has many challenges, such as poor image quality, artifacts, and high lesion variability. Thus, automatic lesion classification may aid professionals in breast cancer diagnosis. In this scope, computer-aided diagnosis systems have been proposed to assist in medical image interpretation, outperforming the intra and inter-observer variability.

Classification of Continuous ECG Segments - Performance Analysis of a Deep Learning Model.

Classification of electrocardiogram (ECG) signals plays an important role in the diagnosis of heart diseases. It is a complex and non-linear signal, which is the first option to preliminary identify specific pathologies/conditions (e.g.

Dual consistency loss for contour-aware segmentation in medical images.

Medical image segmentation is a paramount task for several clinical applications, namely for the diagnosis of pathologies, for treatment planning, and for aiding image-guided surgeries. With the development of deep learning, Convolutional Neural Networks (CNN) have become the state-of-the-art for medical image segmentation. However, issues are still raised concerning the precise object boundary delineation, since traditional CNNs can produce non-smooth segmentations with boundary discontinuities.

Comparative analysis of deep learning methods for lesion detection on full screening mammography.

Breast cancer is the most prevalent type of cancer in women. Although mammography is used as the main imaging modality for the diagnosis, robust lesion detection in mammography images is a challenging task, due to the poor contrast of the lesion boundaries and the widely diverse sizes and shapes of the lesions. Deep Learning techniques have been explored to facilitate automatic diagnosis and have produced outstanding outcomes when used for different medical challenges.

SurgT challenge: Benchmark of soft-tissue trackers for robotic surgery.

This paper introduces the "SurgT: Surgical Tracking" challenge which was organized in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022). There were two purposes for the creation of this challenge: (1) the establishment of the first standardized benchmark for the research community to assess soft-tissue trackers; and (2) to encourage the development of unsupervised deep learning methods, given the lack of annotated data in surgery. A dataset of 157 stereo endoscopic videos from 20 clinical cases, along with stereo camera calibration parameters, have been provided.

Smart scan of medical device displays to integrate with a mHealth application.

Background: The daily monitoring of the physiological parameters is essential for monitoring health condition and to prevent health problems. This is possible due to the democratization of numerous types of medical devices and promoted by the interconnection between these and smartphones. Nevertheless, medical devices that connect to smartphones are typically limited to manufacturers applications.

Exploring the correlations between indicators of COVID-19 and the concentration of pharmaceutical compounds in wastewater treatment plants in Northern Portugal.

The COVID-19 pandemic caused by the SARS-CoV-2 virus led to changes in the lifestyle and human behaviour, which resulted in different consumption patterns of some classes of pharmaceuticals including curative, symptom-relieving, and psychotropic drugs. The trends in the consumption of these compounds are related to their concentrations in wastewater systems, since incompletely metabolised drugs (or their metabolites back transformed into the parental form) may be detected and quantified by analytical methods. Pharmaceuticals are highly recalcitrant compounds and conventional activated sludge processes implemented in wastewater treatment plants (WWTP) are ineffective at degrading these substances.

Remote Monitoring System of Dynamic Compression Bracing to Correct Pectus Carinatum.

Pectus carinatum (PC) is a chest deformity caused by disproportionate growth of the costal cartilages compared with the bony thoracic skeleton, pulling the sternum forwards and leading to its protrusion. Currently, the most common non-invasive treatment is external compressive bracing, by means of an orthosis. While this treatment is widely adopted, the correct magnitude of applied compressive forces remains unknown, leading to suboptimal results.

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition.

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of ‹instrument, verb, target› combination delivers more comprehensive details about the activities taking place in surgical videos.

Augmented Reality-Assisted Ultrasound Breast Biopsy.

Breast cancer is the most prevalent cancer in the world and the fifth-leading cause of cancer-related death. Treatment is effective in the early stages. Thus, a need to screen considerable portions of the population is crucial.

A multi-task convolutional neural network for classification and segmentation of chronic venous disorders.

Chronic Venous Disorders (CVD) of the lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. Due to the exponential growth of the aging population and the worsening of CVD with age, it is expected that the healthcare costs and the resources needed for the treatment of CVD will increase in the coming years. The early diagnosis of CVD is fundamental in treatment planning, while the monitoring of its treatment is fundamental to assess a patient's condition and quantify the evolution of CVD.

Characterization of the Workspace and Limits of Operation of Laser Treatments for Vascular Lesions of the Lower Limbs.

The increase of the aging population brings numerous challenges to health and aesthetic segments. Here, the use of laser therapy for dermatology is expected to increase since it allows for non-invasive and infection-free treatments. However, existing laser devices require doctors' manually handling and visually inspecting the skin.

Classification of Chronic Venous Disorders using an Ensemble Optimization of Convolutional Neural Networks.

Chronic Venous Disorders (CVD) of lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. The early diagnosis of CVD is critical, however, the diagnosis relies on a visual recognition of the various venous disorders which is time- consuming and dependent on the physician's expertise. Thus, automatic strategies for the classification of the CVD severity are claimed.

Deep learning methods for lesion detection on mammography images: a comparative analysis.

Automatic lesion segmentation in mammography images assists in the diagnosis of breast cancer, which is the most common type of cancer especially among women. The robust segmentation of mammography images has been considered a backbreaking task due to: i) the low contrast of the lesion boundaries; ii) the extremely variable lesions' sizes and shapes; and iii) some extremely small lesions on the mammogram image. To overcome these drawbacks, Deep Learning methods have been implemented and have shown impressive results when applied to medical image segmentation.

A deep learning method for kidney segmentation in 2D ultrasound images.

Ultrasound (US) is a medical imaging modality widely used for diagnosis, monitoring, and guidance of surgical procedures. However, the accurate interpretation of US images is a challenging task. Recently, portable 2D US devices enhanced with Artificial intelligence (AI) methods to identify, in real-time, specific organs are widely spreading worldwide.

Ultrasound training simulator using augmented reality glasses: an accuracy and precision assessment study.

Ultrasound (US) imaging despite being safe, cost-effective, and radiation-free, presents poor quality and artifacts, requiring specific medical training in US probe handling and image evaluation. The use of simulators to train physicians has proven its effectiveness, but most of them require specific facilities and hardware. In the last few years, augmented reality has gained relevance to simulate real scenarios which can avoid large setups and broaden medical training to more physicians.