Developing a Canadian artificial intelligence medical curriculum using a Delphi study.

The integration of artificial intelligence (AI) education into medical curricula is critical for preparing future healthcare professionals. This research employed the Delphi method to establish an expert-based AI curriculum for Canadian undergraduate medical students. A panel of 18 experts in health and AI across Canada participated in three rounds of surveys to determine essential AI learning competencies.

Femoral cartilage ultrasound echo-intensity is a valid measure of cartilage composition.

This study aimed to create a conversion equation that accurately predicts cartilage magnetic resonance imaging (MRI) T2 relaxation times using ultrasound echo-intensity and common participant demographics. We recruited 15 participants with a primary anterior cruciate ligament reconstruction between the ages of 18 and 35 years at 1-5 years after surgery. A single investigator completed a transverse suprapatellar scan with the ACLR limb in max knee flexion to image the femoral trochlea cartilage.

Anatomy-aware computed tomography-to-ultrasound spine registration.

Background: Ultrasound (US) has demonstrated to be an effective guidance technique for lumbar spine injections, enabling precise needle placement without exposing the surgeon or the patient to ionizing radiation. However, noise and acoustic shadowing artifacts make US data interpretation challenging. To mitigate these problems, many authors suggested using computed tomography (CT)-to-US registration to align the spine in pre-operative CT to intra-operative US data, thus providing localization of spinal landmarks.

Diffusion models in medical imaging: A comprehensive survey.

Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired noise-free data from noisy data samples. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples in spite of their known computational burdens.

The Big Bang of Deep Learning in Ultrasound-Guided Surgery: A Review.

Ultrasound (US) imaging is a paramount modality in many image-guided surgeries and percutaneous interventions, thanks to its high portability, temporal resolution, and cost-efficiency. However, due to its imaging principles, the US is often noisy and difficult to interpret. Appropriate image processing can greatly enhance the applicability of the imaging modality in clinical practice.

Förster resonance energy transfer efficiency of the vinculin tension sensor in cultured primary cortical neuronal growth cones.

: Interaction of neurons with their extracellular environment and the mechanical forces at focal adhesions and synaptic junctions play important roles in neuronal development. : To advance studies of mechanotransduction, we demonstrate the use of the vinculin tension sensor (VinTS) in primary cultures of cortical neurons. VinTS consists of TS module (TSMod), a Förster resonance energy transfer (FRET)-based tension sensor, inserted between vinculin's head and tail.

Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images.

Objective: To validate a semi-automated technique to segment ultrasound-assessed femoral cartilage without compromising segmentation accuracy to a traditional manual segmentation technique in participants with an anterior cruciate ligament injury (ACL).

Design: We recruited 27 participants with a primary unilateral ACL injury at a pre-operative clinic visit. One investigator performed a transverse suprapatellar ultrasound scan with the participant's ACL injured knee in maximum flexion.

Multi-feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data.

The global pandemic of the novel coronavirus disease 2019 (COVID-19) has put tremendous pressure on the medical system. Imaging plays a complementary role in the management of patients with COVID-19. Computed tomography (CT) and chest X-ray (CXR) are the two dominant screening tools.

KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation.

Most methods for medical image segmentation use U-Net or its variants as they have been successful in most of the applications. After a detailed analysis of these "traditional" encoder-decoder based approaches, we observed that they perform poorly in detecting smaller structures and are unable to segment boundary regions precisely. This issue can be attributed to the increase in receptive field size as we go deeper into the encoder.

The Rheology of the Carotid Sinus: A Path Toward Bioinspired Intervention.

The association between blood viscosity and pathological conditions involving a number of organ systems is well known. However, how the body measures and maintains appropriate blood viscosity is not well-described. The literature endorsing the function of the carotid sinus as a site of baroreception can be traced back to some of the earliest descriptions of digital pressure on the neck producing a drop in blood delivery to the brain.

Bone shadow segmentation from ultrasound data for orthopedic surgery using GAN.

Purpose: Real-time, two (2D) and three-dimensional (3D) ultrasound (US) has been investigated as a potential alternative to fluoroscopy imaging in various surgical and non-surgical orthopedic procedures. However, low signal to noise ratio, imaging artifacts and bone surfaces appearing several millimeters (mm) in thickness have hindered the wide spread adaptation of this safe imaging modality. Limited field of view and manual data collection cause additional problems during US-based orthopedic procedures.

Robust real-time bone surfaces segmentation from ultrasound using a local phase tensor-guided CNN.

Purpose: Automatic bone surfaces segmentation is one of the fundamental tasks of ultrasound (US)-guided computer-assisted orthopedic surgery procedures. However, due to various US imaging artifacts, manual operation of the transducer during acquisition, and different machine settings, many existing methods cannot deal with the large variations of the bone surface responses, in the collected data, without manual parameter selection. Even for fully automatic methods, such as deep learning-based methods, the problem of dataset bias causes networks to perform poorly on the US data that are different from the training set.

Knee-Cartilage Segmentation and Thickness Measurement from 2D Ultrasound.

Ultrasound (US) could become a standard of care imaging modality for the quantitative assessment of femoral cartilage thickness for the early diagnosis of knee osteoarthritis. However, low contrast, high levels of speckle noise, and various imaging artefacts hinder the analysis of collected data. Accurate, robust, and fully automatic US image-enhancement and cartilage-segmentation methods are needed in order to improve the widespread deployment of this imaging modality for knee-osteoarthritis diagnosis and monitoring.

Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN.

Purpose: Ultrasound (US) provides real-time, two-/three-dimensional safe imaging. Due to these capabilities, it is considered a safe alternative to intra-operative fluoroscopy in various computer-assisted orthopedic surgery (CAOS) procedures. However, interpretation of the collected bone US data is difficult due to high levels of noise, various imaging artifacts, and bone surfaces response appearing several millimeters (mm) in thickness.

3D Ultrasound for Orthopedic Interventions.

Ultrasound is a real-time, non-radiation-based imaging modality with an ability to acquire two-dimensional (2D) and three-dimensional (3D) data. Due to these capabilities, research has been carried out in order to incorporate it as an intraoperative imaging modality for various orthopedic surgery procedures. However, high levels of noise, different imaging artifacts, and bone surfaces appearing blurred with several mm in thickness have prohibited the widespread use of ultrasound as a standard of care imaging modality in orthopedics.

Convolution neural networks for real-time needle detection and localization in 2D ultrasound.

Purpose: We propose a framework for automatic and accurate detection of steeply inserted needles in 2D ultrasound data using convolution neural networks. We demonstrate its application in needle trajectory estimation and tip localization.

Methods: Our approach consists of a unified network, comprising a fully convolutional network (FCN) and a fast region-based convolutional neural network (R-CNN).

Signal attenuation maps for needle enhancement and localization in 2D ultrasound.

Purpose: We propose a novel framework for enhancement and localization of steeply inserted hand-held needles under in-plane 2D ultrasound guidance.

Methods: Depth-dependent attenuation and non-axial specular reflection hinder visibility of steeply inserted needles. Here, we model signal transmission maps representative of the attenuation probability within the image domain.