Assessing competency in less invasive surfactant administration: simulation-based validity evidence for the LISA-AT scores.

Background: The Less Invasive Surfactant Administration Assessment Tool (LISA-AT) was developed to support operator training and competence assessment. This study aimed to gather validity evidence in the simulated setting to support using the LISA-AT scores.

Methods: Validity evidence was gathered using the Messick framework.

Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation.

We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, using a prospective cross-center, multi-level approach. We developed, implemented, and tested a deep-learning model for fetal growth scans using both retrospective and prospective data. We used a modified Progressive Concept Bottleneck Model with pre-established clinical concepts as explanations (feedback on image optimization and presence of anatomical landmarks) as well as segmentations (outlining anatomical landmarks).

Video-based robotic surgical action recognition and skills assessment on porcine models using deep learning.

Objectives: This study aimed to develop an automated skills assessment tool for surgical trainees using deep learning.

Background: Optimal surgical performance in robot-assisted surgery (RAS) is essential for ensuring good surgical outcomes. This requires effective training of new surgeons, which currently relies on supervision and skill assessment by experienced surgeons.

Examining the learning curves in robotic cardiac surgery wet lab simulation training.

Background: Simulation-based training has gained distinction in cardiothoracic surgery as robotic-assisted cardiac procedures evolve. Despite the increasing use of wet lab simulators, the effectiveness of these training methods and skill acquisition rates remain poorly understood.

Objectives: This study aimed to compare learning curves and assess the robotic cardiac surgical skill acquisition rate for cardiac and noncardiac surgeons who had no robotic experience in a wet lab simulation setting.

A change of mind: Error motivation is shaped by error perceptions in different learning environments.

Background: The medical profession has traditionally had a culture of "blame and shame," despite the importance errors have for learning, motivation, and improvement of clinical skills. This study aimed to explore how medical students and newly graduated doctors perceive errors across different learning contexts and levels of expertise, and how error perceptions influence motivation and engagement in learning activities.

Methods: Semi-structured interviews were conducted, and thematic analysis was used to identify themes.

Teledermoscopic triage of melanoma-suspicious skin lesions is safe: A retrospective comparative diagnostic accuracy study with multiple assessors.

Background: The rising incidence of melanoma and the high number of benign lesions excised due to diagnostic uncertainty highlight the need for effective patient triage. This study assesses the safety and accuracy of teledermoscopic triage on a high-prevalence case set with pre-triaged, challenging, melanoma-suspicious lesions.

Methods: Five dermatologists independently reviewed 250 retrospectively extracted patient cases.

Automated performance metrics and surgical gestures: two methods for assessment of technical skills in robotic surgery.

The objective of this study is to compare automated performance metrics (APM) and surgical gestures for technical skills assessment during simulated robot-assisted radical prostatectomy (RARP). Ten novices and six experienced RARP surgeons performed simulated RARPs on the RobotiX Mentor (Surgical Science, Sweden). Simulator APM were automatically recorded, and surgical videos were manually annotated with five types of surgical gestures.

Classifying Inflammation on Intestinal Ultrasound Images and Cineloops - A Learning Curve Study.

Background And Aims: Intestinal ultrasound has become a crucial tool for assessing inflammation in patients with inflammatory bowel disease, prompting a surge in demand for trained sonographers. While educational programs exist, the length of training needed to reach proficiency in correctly classifying inflammation remains unclear. Our study addresses this gap partly by exploring the learning curves associated with the deliberate practice of sonographic disease assessment, focusing on the key disease activity parameters of bowel wall thickness, bowel wall stratification, color Doppler signal, and inflammatory fat.

Clinical implementation of first trimester screening for congenital heart defects.

Objective: To examine the feasibility and performance of implementing a standardized fetal cardiac scan at the time of a routine first-trimester ultrasound scan.

Method: A retrospective, single-center study in an unselected population between March 2021 and July 2022. A standardized cardiac scan protocol consisting of a four-chamber and 3-vessel trachea view with color Doppler was implemented as part of the routine first-trimester scan.

How to Use and Report on -values.

The use of the p-value in quantitative research, particularly its threshold of "P < 0.05" for determining "statistical significance," has long been a cornerstone of statistical analysis in research. However, this standard has been increasingly scrutinized for its potential to mislead findings, especially when the practical significance, the number of comparisons, or the suitability of statistical tests are not properly considered.

AI supported fetal echocardiography with quality assessment.

This study aimed to develop a deep learning model to assess the quality of fetal echocardiography and to perform prospective clinical validation. The model was trained on data from the 18-22-week anomaly scan conducted in seven hospitals from 2008 to 2018. Prospective validation involved 100 patients from two hospitals.

Technical Skills Curriculum in Neonatology: A Modified European Delphi Study.

Introduction: Simulation-based training (SBT) aids healthcare providers in acquiring the technical skills necessary to improve patient outcomes and safety. However, since SBT may require significant resources, training all skills to a comparable extent is impractical. Hence, a strategic prioritization of technical skills is necessary.

Role of artificial-intelligence-assisted automated cardiac biometrics in prenatal screening for coarctation of aorta.

Objective: Although remarkable strides have been made in fetal medicine and the prenatal diagnosis of congenital heart disease, around 60% of newborns with isolated coarctation of the aorta (CoA) are not identified prior to birth. The prenatal detection of CoA has been shown to have a notable impact on survival rates of affected infants. To this end, implementation of artificial intelligence (AI) in fetal ultrasound may represent a groundbreaking advance.

Data sharing and big data in health professions education: Ottawa consensus statement and recommendations for scholarship.

Changes in digital technology, increasing volume of data collection, and advances in methods have the potential to unleash the value of big data generated through the education of health professionals. Coupled with this potential are legitimate concerns about how data can be used or misused in ways that limit autonomy, equity, or harm stakeholders. This consensus statement is intended to address these issues by foregrounding the ethical imperatives for engaging with big data as well as the potential risks and challenges.

Surgical gestures can be used to assess surgical competence in robot-assisted surgery : A validity investigating study of simulated RARP.

To collect validity evidence for the assessment of surgical competence through the classification of general surgical gestures for a simulated robot-assisted radical prostatectomy (RARP). We used 165 video recordings of novice and experienced RARP surgeons performing three parts of the RARP procedure on the RobotiX Mentor. We annotated the surgical tasks with different surgical gestures: dissection, hemostatic control, application of clips, needle handling, and suturing.

Simulation-based assessment of robotic cardiac surgery skills: An international multicenter, cross-specialty trial.

Objective: This study aimed to investigate the validity of simulation-based assessment of robotic-assisted cardiac surgery skills using a wet lab model, focusing on the use of a time-based score (TBS) and modified Global Evaluative Assessment of Robotic Skills (mGEARS) score.

Methods: We tested 3 wet lab tasks (atrial closure, mitral annular stitches, and internal thoracic artery [ITA] dissection) with both experienced robotic cardiac surgeons and novices from multiple European centers. The tasks were assessed using 2 tools: TBS and mGEARS score.

Validating the virtual: a deep dive into ultrasound simulator metrics in otorhinolaryngology.

Purpose: This study aimed to assess the validity of simulation-based assessment of ultrasound skills for thyroid ultrasound.

Methods: The study collected validity evidence for simulation-based ultrasound assessment of thyroid ultrasound skills. Experts (n = 8) and novices (n = 21) completed a test containing two tasks and four cases on a virtual reality ultrasound simulator (U/S Mentor's Neck Ultrasound Module).

Simulation-based assessment of upper abdominal ultrasound skills.

Background: Ultrasound is a safe and effective diagnostic tool used within several specialties. However, the quality of ultrasound scans relies on sufficiently skilled clinician operators. The aim of this study was to explore the validity of automated assessments of upper abdominal ultrasound skills using an ultrasound simulator.

Learning strategies for the advanced trainee in specialist training.

Background: A significant factor of clinicians' learning is based on their ability to effectively transfer acquired knowledge, skills, and attitudes from specialty-specific clinical courses to their working environment.

Material And Method: We conducted semi-structured interviews with 20 anaesthesiologist trainees (i.e.

Cost of simulation-based mastery learning for abdominal ultrasound.

Background: Ultrasound is an essential diagnostic examination used in several medical specialties. However, the quality of ultrasound examinations is dependent on mastery of certain skills, which may be difficult and costly to attain in the clinical setting. This study aimed to explore mastery learning for trainees practicing general abdominal ultrasound using a virtual reality simulator and to evaluate the associated cost per student achieving the mastery learning level.