- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

The Gap Between Expected and Perceived Organizational Culture in an Iranian University of Medical Sciences from the Perspective of Different Stakeholder Groups.

Background: Organizational culture plays a key role in the success of organizations. Therefore, it is important to measure the gap between stakeholders' perceptions and expectations of the dominant culture in that organization. This study investigates the gap between the perceptions and expectations of the academic community at the Iran University of Medical Sciences.

Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications.

Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property prediction and brain connectome analysis. Importantly, recent works have shown that considering relationships between input data samples has a positive regularizing effect on the downstream task in healthcare applications.

ToxNet: an artificial intelligence designed for decision support for toxin prediction.

Background: Artificial intelligences (AIs) are emerging in the field of medical informatics in many areas. They are mostly used for diagnosis support in medical imaging but have potential uses in many other fields of medicine where large datasets are available.

Aim: To develop an artificial intelligence (AI) "ToxNet", a machine-learning based computer-aided diagnosis (CADx) system, which aims to predict poisons based on patient's symptoms and metadata from our Poison Control Center (PCC) data.

IE-Vnet: Deep Learning-Based Segmentation of the Inner Ear's Total Fluid Space.

Background: MR-based high-resolution volumetric quantification methods of the endolymphatic hydrops (ELH) are highly dependent on a reliable segmentation of the inner ear's total fluid space (TFS). This study aimed to develop a novel open-source inner ear TFS segmentation approach using a dedicated deep learning (DL) model.

Methods: The model was based on a V-Net architecture (IE-Vnet) and a multivariate (MR scans: T1, T2, FLAIR, SPACE) training dataset (D1, 179 consecutive patients with peripheral vestibulocochlear syndromes).

Differentiable Graph Module (DGM) for Graph Convolutional Networks.

Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-euclidean structured data. Such methods have shown promising results on a broad spectrum of applications ranging from social science, biomedicine, and particle physics to computer vision, graphics, and chemistry. One of the limitations of the majority of current graph neural network architectures is that they are often restricted to the transductive setting and rely on the assumption that the underlying graph is known and fixed.

Persistent horizontal and vertical, MR-induced nystagmus in resting state Human Connectome Project data.

Objective: Strong magnetic fields from magnetic resonance (MR) scanners induce a Lorentz force that contributes to vertigo and persistent nystagmus. Prior studies have reported a predominantly horizontal direction for healthy subjects in a 7 Tesla (T) MR scanner, with slow phase velocity (SPV) dependent on head orientation. Less is known about vestibular signal behavior for subjects in a weaker, 3T magnetic field, the standard strength used in the Human Connectome Project (HCP).

Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer.

Background: The evaluation of automatic segmentation algorithms is commonly performed using geometric metrics. An analysis based on dosimetric parameters might be more relevant in clinical practice but is often lacking in the literature. The aim of this study was to investigate the impact of state-of-the-art 3D U-Net-generated organ delineations on dose optimization in radiation therapy (RT) for prostate cancer patients.

A comparative study of the status of supportive-palliative care provision in Iran and selected countries: Strengths and weaknesses.

Background: Terminally, illnesses such as cancer, AIDS, dementia, and advanced heart disease will require special supportive and palliative care, although a few numbers of these patients are provided with these services. The aim of the present study was to perform a comparative study of supportive-palliative care provision in selected countries.

Materials And Methods: This research was a descriptive comparative study that its research population was the frameworks of palliative and supportive care provision in Egypt, Turkey, America, Australia, Canada, the Netherlands, and China.

Physics-aware learning and domain-specific loss design in ophthalmology.

The human cataract, a developing opacification of the human eye lens, currently constitutes the world's most frequent cause for blindness. As a result, cataract surgery has become the most frequently performed ophthalmic surgery in the world. By removing the human lens and replacing it with an artificial intraocular lens (IOL), the optical system of the eye is restored.

Investigating the challenges and barriers of palliative care delivery in Iran and the World: A systematic review study.

Palliative care and terminal patients care centers have an important role in improving the physical and psychological state of the patient and their families and increasing their satisfaction and care providers. A literature search of online databases (PubMed, Scopus, Web of science, Cochrane library, and Google Scholar) was searched from January 1, 2000, to the end of April 2019, by using the appropriate English keywords. Furthermore, IranMedex, Barkat, and Magiran databases were searched for the Persian articles.

Using Base-ml to Learn Classification of Common Vestibular Disorders on DizzyReg Registry Data.

Multivariable analyses (MVA) and machine learning (ML) applied on large datasets may have a high potential to provide clinical decision support in neuro-otology and reveal further avenues for vestibular research. To this end, we build base-ml, a comprehensive MVA/ML software tool, and applied it to three increasingly difficult clinical objectives in differentiation of common vestibular disorders, using data from a large prospective clinical patient registry (DizzyReg). Base-ml features a full MVA/ML pipeline for classification of multimodal patient data, comprising tools for data loading and pre-processing; a stringent scheme for nested and stratified cross-validation including hyper-parameter optimization; a set of 11 classifiers, ranging from commonly used algorithms like logistic regression and random forests, to artificial neural network models, including a graph-based deep learning model which we recently proposed; a multi-faceted evaluation of classification metrics; tools from the domain of "Explainable AI" that illustrate the input distribution and a statistical analysis of the most important features identified by multiple classifiers.

Simultaneous imputation and classification using Multigraph Geometric Matrix Completion (MGMC): Application to neurodegenerative disease classification.

Large-scale population-based studies in medicine are a key resource towards better diagnosis, monitoring, and treatment of diseases. They also serve as enablers of clinical decision support systems, in particular computer-aided diagnosis (CADx) using machine learning (ML). Numerous ML approaches for CADx have been proposed in literature.

Metabolic connectivity-based single subject classification by multi-regional linear approximation in the rat.

Metabolic connectivity patterns on the basis of [F]-FDG positron emission tomography (PET) are used to depict complex cerebral network alterations in different neurological disorders and therefore may have the potential to support diagnostic decisions. In this study, we established a novel statistical classification method taking advantage of differential time-dependent states of whole-brain metabolic connectivity following unilateral labyrinthectomy (UL) in the rat and explored its classification accuracy. The dataset consisted of repeated [F]-FDG PET measurements at baseline and 1, 3, 7, and 15 days (= maximum of 5 classes) after UL with 17 rats per measurement day.

IE-Map: a novel in-vivo atlas and template of the human inner ear.

Brain atlases and templates are core tools in scientific research with increasing importance also in clinical applications. Advances in neuroimaging now allowed us to expand the atlas domain to the vestibular and auditory organ, the inner ear. In this study, we present IE-Map, an in-vivo template and atlas of the human labyrinth derived from multi-modal high-resolution magnetic resonance imaging (MRI) data, in a fully non-invasive manner without any contrast agent or radiation.

Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders.

Background: Diagnostic classification of central vs. peripheral etiologies in acute vestibular disorders remains a challenge in the emergency setting. Novel machine-learning methods may help to support diagnostic decisions.

Analyzing the co-localization of substantia nigra hyper-echogenicities and iron accumulation in Parkinson's disease: A multi-modal atlas study with transcranial ultrasound and MRI.

Background: Transcranial B-mode sonography (TCS) can detect hyperechogenic speckles in the area of the substantia nigra (SN) in Parkinson's disease (PD). These speckles correlate with iron accumulation in the SN tissue, but an exact volumetric localization in and around the SN is still unknown. Areas of increased iron content in brain tissue can be detected in vivo with magnetic resonance imaging, using quantitative susceptibility mapping (QSM).

Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and sway.

We perform classification, ranking and mapping of body sway parameters from static posturography data of patients using recent machine-learning and data-mining techniques. Body sway is measured in 293 individuals with the clinical diagnoses of acute unilateral vestibulopathy (AVS, n = 49), distal sensory polyneuropathy (PNP, n = 12), anterior lobe cerebellar atrophy (CA, n = 48), downbeat nystagmus syndrome (DN, n = 16), primary orthostatic tremor (OT, n = 25), Parkinson's disease (PD, n = 27), phobic postural vertigo (PPV n = 59) and healthy controls (HC, n = 57). We classify disorders and rank sway features using supervised machine learning.

DeepVOG: Open-source pupil segmentation and gaze estimation in neuroscience using deep learning.

Background: A prerequisite for many eye tracking and video-oculography (VOG) methods is an accurate localization of the pupil. Several existing techniques face challenges in images with artifacts and under naturalistic low-light conditions, e.g.

A baseline study for detection of Parkinson's disease with 3D-transcranial sonography and uni-lateral reconstruction.

Introduction: TCS is a well-established technique for diagnosis of Parkinson's disease (PD). Volumetric 3D-TCS is a promising complementary approach for objective acquisition and analysis, in particular for less experienced sonographers. This study provides baselines for Parkinson detection (sensitivity and specificity), cutoff values and inter-rater agreement in 3D-TCS.

Cloud Deployment of High-Resolution Medical Image Analysis With TOMAAT.

Background: Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art, most of the methods cannot be easily accessed, compared or used by other researchers or clinicians. Even if developers publish their code and pre-trained models on the internet, integration in stand-alone applications and existing workflows is often not straightforward, especially for clinical research partners.

MR imaging differentiation of Fe and Fe based on relaxation and magnetic susceptibility properties.

Purpose: The aim of this study is to evaluate the MR imaging behavior of ferrous (Fe) and ferric (Fe) iron ions in order to develop a noninvasive technique to quantitatively differentiate between both forms of iron.

Methods: MRI was performed at 3 T in a phantom consisting of 21 samples with different concentrations of ferrous and ferric chloride solutions (between 0 and 10 mmol/L). A multi-echo spoiled gradient-echo pulse sequence with eight echoes was used for both T * and quantitative susceptibility measurements.

The role of the pallidothalamic fibre tracts in deep brain stimulation for dystonia: A diffusion MRI tractography study.

Background: Deep Brain Stimulation (DBS) of the Globus pallidus internus (GPi) is gold standard treatment in medically refractory dystonia. Recent evidence indicates that stimulation effects are also due to axonal modulation and affection of a fibre network. For the GPi, the pallidothalamic tracts are known to be the major motor efferent pathways.

A diffusion-based connectivity map of the GPi for optimised stereotactic targeting in DBS.

Background: The GPi (globus pallidus internus) is an important target nucleus for Deep Brain Stimulation (DBS) in medically refractory movement disorders, in particular dystonia and Parkinson's disease. Beneficial clinical outcome critically depends on precise electrode localization. Recent evidence indicates that not only neurons, but also axonal fibre tracts contribute to promoting the clinical effect.

Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography.

Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database.