Upskilling or deskilling? Measurable role of an AI-supported training for radiology residents: a lesson from the pandemic.

Objectives: This article aims to evaluate the use and effects of an artificial intelligence system supporting a critical diagnostic task during radiology resident training, addressing a research gap in this field.

Materials And Methods: We involved eight residents evaluating 150 CXRs in three scenarios: no AI, on-demand AI, and integrated-AI. The considered task was the assessment of a multi-regional severity score of lung compromise in patients affected by COVID-19.

3D Scanning of Surgical Specimens to Improve Communication Between Surgeon and Pathologist: A Head and Neck Pilot Study.

Successful surgical outcomes in head and neck cancer depend on the accurate identification of resection margins. Effective communication between surgeons and pathologists is critical, but is often jeopardised by challenges in sampling and orienting anatomically complex specimens. This pilot study aims to evaluate the use of 3D scanning of surgical specimens as a tool to improve communication and optimise the pathology sampling process.

Intraoperative surgical navigation improves margin status in advanced malignancies of the anterior craniofacial area: A prospective observational study with systematic review of the literature and meta-analysis.

The current scientific evidence suggests that surgical navigation (SN) can contribute to improve oncologic outcomes in sinonasal and craniofacial surgery. The present study investigated the feasibility of intraoperative SN and its role in improving the outcomes of surgically treated sinonasal and craniofacial tumors. This prospective study compared navigation-guided surgery for sinonasal or craniofacial malignancies with a pair-matched cohort (1:2 matching) of patients operated without SN.

Deep learning to predict long-term mortality from plain chest X-ray in patients referred for suspected coronary artery disease.

Background: The hypothesis that a deep learning (DL) model can produce long-term prognostic information from chest X-ray (CXR) has already been confirmed within cancer screening programs. We summarize our experience with DL prediction of long-term mortality, from plain CXR, in patients referred for angina and coronary angiography.

Methods: Data of patients referred to an Italian academic hospital were analyzed retrospectively.

Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data.

Many clinical and research studies of the human brain require accurate structural MRI segmentation. While traditional atlas-based methods can be applied to volumes from any acquisition site, recent deep learning algorithms ensure high accuracy only when tested on data from the same sites exploited in training (i.e.

Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology.

Full Laboratory Automation is revolutionizing work habits in an increasing number of clinical microbiology facilities worldwide, generating huge streams of digital images for interpretation. Contextually, deep learning architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic bacterial culture plates, including presumptive pathogen identification.

CineScale2: a dataset of cinematic camera features in movies.

The position and orientation of the camera in relation to the subject(s) in a movie scene, namely and , are essential features in the film-making process due to their influence on the viewer's perception of the scene. We provide a database containing camera feature annotations on camera angle and camera level, for about 25,000 image frames. Frames are sampled from a wide range of movies, freely available images, and shots from cinematographic websites, and are annotated on the following five categories - Overhead, High, Neutral, Low, and Dutch - for what concerns camera angle, and on six different classes of camera level: Aerial, Eye, Shoulder, Hip, Knee, and Ground level.

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients.

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does "trustworthy AI" mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays.

Deep learning to detect significant coronary artery disease from plain chest radiographs AI4CAD.

Background: The predictive role of chest radiographs in patients with suspected coronary artery disease (CAD) is underestimated and may benefit from artificial intelligence (AI) applications.

Objectives: To train, test, and validate a deep learning (DL) solution for detecting significant CAD based on chest radiographs.

Methods: Data of patients referred for angina and undergoing chest radiography and coronary angiography were analysed retrospectively.

Development of a cadaveric head and neck cancer model and three-dimensional analysis of margins in surgical navigation-aided ablations.

Introduction: The adequacy of the surgical resection is the main controllable variable that is in the hands of the surgical team. There exists an unmet need to increase the rate of negative margins, particularly in cancers invading the craniofacial area. The study aimed 1) at developing a gross tumor model to be utilized for research, educational, and training purposes and 2) establishing the 3-dimensional relationship between the outer surface of the surgical specimen and tumor surface and test the effect of guiding ablations on cadavers with surgical navigation (SN).

DenseMatch: a dataset for real-time 3D reconstruction.

We provide a database aimed at real-time quantitative analysis of 3D reconstruction and alignment methods, containing 3140 point clouds from 10 subjects/objects. These scenes are acquired with a high-resolution 3D scanner. It contains depth maps that produce point clouds with more than 500k points on average.

Effects of remote digital monitoring on oral hygiene of orthodontic patients: a prospective study.

Background: Remote digital monitoring during orthodontic treatment can help patients in improving their oral hygiene performance and reducing the number of appointments due to emergency reasons, especially in time of COVID-19 pandemic where non-urgent appointments might be discouraged.

Methods: Thirty patients scheduled to start an orthodontic treatment were divided into two groups of fifteen. Compared to controls, study group patients were provided with scan box and cheek retractor (Dental Monitoring®) and were instructed to take monthly intra-oral scans.

CineScale: A dataset of cinematic shot scale in movies.

We provide a database containing shot scale annotations (i.e., the apparent distance of the camera from the subject of a filmed scene) for more than 792,000 image frames.

Attitude towards Telemonitoring in Orthodontists and Orthodontic Patients.

The purpose of this study was to analyze the attitude of dentists and patients towards the use of Dental Monitoring (DM), an orthodontic telemonitoring software. Thus, two different specially prepared specific questionnaires were administered to 80 dentists (40 were general dentists and 40 orthodontists) and 80 orthodontic patients. All dentists judged positively telemonitoring, as 96.

Automatic classification of mice vocalizations using Machine Learning techniques and Convolutional Neural Networks.

Ultrasonic vocalizations (USVs) analysis is a well-recognized tool to investigate animal communication. It can be used for behavioral phenotyping of murine models of different disorders. The USVs are usually recorded with a microphone sensitive to ultrasound frequencies and they are analyzed by specific software.

Spatial-Spectral Evidence of Glare Influence on Hyperspectral Acquisitions.

Glare is an unwanted optical phenomenon which affects imaging systems with optics. This paper presents for the first time a set of hyperspectral image (HSI) acquisitions and measurements to verify how glare affects acquired HSI data in standard conditions. We acquired two ColorCheckers (CCs) in three different lighting conditions, with different backgrounds, different exposure times, and different orientations.

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI.

Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Alternatively, recent methods deploying solutions based on Convolutional Neural Networks (CNNs) are enabling the direct analysis of out-of-the-scanner data.

Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review.

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial-spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies.

Automatic hemolysis identification on aligned dual-lighting images of cultured blood agar plates.

Background And Objective: The recent introduction of Full Laboratory Automation systems in clinical microbiology opens to the availability of streams of high definition images representing bacteria culturing plates. This creates new opportunities to support diagnostic decisions through image analysis and interpretation solutions, with an expected high impact on the efficiency of the laboratory workflow and related quality implications. Starting from images acquired under different illumination settings (top-light and back-light), the objective of this work is to design and evaluate a method for the detection and classification of diagnostically relevant hemolysis effects associated with specific bacteria growing on blood agar plates.

Concept and Design of a 3D Printed Support to Assist Hand Scanning for the Realization of Customized Orthosis.

In the rehabilitation field, the use of additive manufacturing techniques to realize customized orthoses is increasingly widespread. Obtaining a 3D model for the 3D printing phase can be done following different methodologies. We consider the creation of personalized upper limb orthoses, also including fingers, starting from the acquisition of the hand geometry through accurate 3D scanning.

Mesh Denoising with (Geo)Metric Fidelity.

Working with noisy meshes and aiming at providing high-fidelity 3D object models without tampering the metric quality of the acquisitions, we propose a mesh denoising technique that, through a normal-diffusion process guided by a curvature saliency map, is able to preserve and emphasize the natural object features, concurrently allowing the introduction of a bound on the maximum distance from the original model. Moreover, both the position of the mesh vertices and the edge orientations are optimized through a tailored geometric-aliasing correction. Thanks to an efficiently parallelized procedure, we are able to process even large models almost instantly with a parameter configuration that does not depend on the scale of the object.

Hyperspectral image analysis for rapid and accurate discrimination of bacterial infections: A benchmark study.

With the rapid diffusion of Full Laboratory Automation systems, Clinical Microbiology is currently experiencing a new digital revolution. The ability to capture and process large amounts of visual data from microbiological specimen processing enables the definition of completely new objectives. These include the direct identification of pathogens growing on culturing plates, with expected improvements in rapid definition of the right treatment for patients affected by bacterial infections.

Biological application of Compressed Sensing Tomography in the Scanning Electron Microscope.

The three-dimensional tomographic reconstruction of a biological sample, namely collagen fibrils in human dermal tissue, was obtained from a set of projection-images acquired in the Scanning Electron Microscope. A tailored strategy for the transmission imaging mode was implemented in the microscope and proved effective in acquiring the projections needed for the tomographic reconstruction. Suitable projection alignment and Compressed Sensing formulation were used to overcome the limitations arising from the experimental acquisition strategy and to improve the reconstruction of the sample.