Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging

The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review

Machine learning (ML) has revolutionized medical image-based diagnostics. In this review, we cover a rapidly emerging field that can be potentially significantly impacted by ML - eye tracking in medical imaging. The review investigates the clinical, algorithmic, and hardware properties of the existing studies. In particular, it evaluates 1) the type of eye-tracking equipment used and how the equipment aligns with study aims; 2) the software required to record and process eye-tracking data, which often requires user interface development, and controller command and voice recording; 3) the ML methodology utilized depending on the anatomy of interest, gaze data representation, and target clinical application. The review concludes with a summary of recommendations for future studies, and confirms that the inclusion of gaze data broadens the ML applicability in Radiology from computer-aided diagnosis (CAD) to gaze-based image annotation, physicians' error detection, fatigue recognition, and other areas of potentially high research and clinical impact.

A cognitive deep learning approach for medical image processing

In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the cognitive DL retinal blood vessel segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a matched filter (MF) for feature enhancement and a post-processing phase employing morphological techniques (MT) for refining the segmentation output. Also, the model incorporates multi-scale line detection and scale space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, retinal blood vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation.

Assessing workload in using electromyography (EMG)-based prostheses

Using prosthetic devices requires a substantial cognitive workload. This study investigated classification models for assessing cognitive workload in electromyography (EMG)-based prosthetic devices with various types of input features including eye-tracking measures, task performance, and cognitive performance model (CPM) outcomes. Features selection algorithm, hyperparameter tuning with grid search, and k-fold cross-validation were applied to select the most important features and find the optimal models. Classification accuracy, the area under the receiver operation characteristic curve (AUC), precision, recall, and F1 scores were calculated to compare the models' performance. The findings suggested that task performance measures, pupillometry data, and CPM outcomes, combined with the naïve bayes (NB) and random forest (RF) algorithms, are most promising for classifying cognitive workload. The proposed algorithms can help manufacturers/clinicians predict the cognitive workload of future EMG-based prosthetic devices in early design phases.Practitioner summary: This study investigated the use of machine learning algorithms for classifying the cognitive workload of prosthetic devices. The findings suggested that the models could predict workload with high accuracy and low computational cost and could be used in assessing the usability of prosthetic devices in the early phases of the design process.Abbreviations: 3d: 3 dimensional; ADL: Activities for daily living; ANN: Artificial neural network; AUC: Area under the receiver operation characteristic curve; CC: Continuous control; CPM: Cognitive performance model; CPM-GOMS: Cognitive-Perceptual-Motor GOMS; CRT: Clothespin relocation test; CV: Cross validation; CW: Cognitive workload; DC: Direct control; DOF: Degrees of freedom; ECRL: Extensor carpi radialis longus; ED: Extensor digitorum; EEG: Electroencephalogram; EMG: Electromyography; FCR: Flexor carpi radialis; FD: Flexor digitorum; GOMS: Goals, Operations, Methods, and Selection Rules; LDA: Linear discriminant analysis; MAV: Mean absolute value; MCP: Metacarpophalangeal; ML: Machine learning; NASA-TLX: NASA task load index; NB: Naïve Bayes; PCPS: Percent change in pupil size; PPT: Purdue Pegboard Test; PR: Pattern recognition; PROS-TLX: Prosthesis task load index; RF: Random forest; RFE: Recursive feature selection; SHAP: Southampton hand assessment protocol; SFS: Sequential feature selection; SVC: Support vector classifier.

Metacognitive prompts and numerical ordinality in solving word problems: An eye-tracking study

BACKGROUND

The ability to translate concrete manipulatives into abstract mathematical formulas can aid in the solving of mathematical word problems among students, and metacognitive prompts play a significant role in enhancing this process.

AIMS

Based on the concept of semantic congruence, we explored the effects of metacognitive prompts and numerical ordinality on information searching and cognitive processing, throughout the process of solving mathematical word problems among primary school students in China.

SAMPLE

Participants included 73 primary school students (38 boys and 35 girls) with normal or corrected visual acuity.

METHODS

This study was based on a 2 (prompt information: no-prompt, metacognitive-prompt) × 2 (number attribute: cardinal number, ordinal number) mixed experimental design. We analysed multiple eye-movement indices, such as fixation duration, saccadic amplitude, and pupil size, since they pertained to the areas of interest.

RESULTS

When solving both types of problems, pupil sizes were significantly smaller under the metacognitive-prompt condition compared with the no-prompt condition, and shorter dwell time for specific sentences, conditional on metacognitive prompts, indicated the optimization of the presented algorithm. Additionally, the levels of fixation durations and saccadic amplitudes were significantly higher when solving ordinal number word problems compared with solving ordinal number problems, indicating that primary school students were less efficient in reading and faced increased levels of difficulty when solving ordinal number problems.

CONCLUSIONS

The results indicate that for Chinese upper-grade primary school students, cognitive load was lower in the metacognitive prompting condition and when solving cardinal problems, and higher when solving ordinal problems.

Cognitive Ergonomics: A Review of Interventions for Outpatient Practice

Doctoring is difficult mental work, involving many cognitively demanding processes such as diagnosing, decision-making, parallel processing, communicating, and managing the emotions of others. According to cognitive load theory (CLT), working memory is a limited cognitive resource that can support a finite amount of cognitive load. While the intrinsic cognitive load is the innate load associated with a task, the extraneous load is generated by inefficiency or suboptimal work conditions. Causes of extraneous cognitive load in healthcare include inefficiency, distractions, interruptions, multitasking, stress, poor communication, conflict, and incivility. High levels of cognitive load are associated with impaired function and an increased risk of burnout among physicians. Cognitive ergonomics is the branch of human factors and ergonomics (HFE) focused on supporting the cognitive processes of individuals within a system. In health care, where the cognitive burden on physicians is high, cognitive ergonomics can establish practices and systems that decrease extraneous cognitive load and support pertinent cognitive processes. In this review, we present cognitive ergonomics as a useful framework for conceptualizing an oft-overlooked dimension of labor and apply theory to practice by summarizing evidence-based cognitive ergonomics interventions for outpatient care settings. Our proposed interventions are structured within four general recommendations: 1. minimize distractions, interruptions, and multitasking; 2. optimize the use of the electronic health record (EHR); 3. optimize the use of health information systems (HIS); and 4. support good communication and teamwork. Best practices in cognitive ergonomics can benefit patients, minimize practice inefficiency, and support physician career longevity.

Skill, or Style? Classification of Fetal Sonography Eye-Tracking Data

We present a method for classifying human skill at fetal ultrasound scanning from eye-tracking and pupillary data of sonographers. Human skill characterization for this clinical task typically creates groupings of clinician skills such as expert and beginner based on the number of years of professional experience; experts typically have more than 10 years and beginners between 0-5 years. In some cases, they also include trainees who are not yet fully-qualified professionals. Prior work has considered eye movements that necessitates separating eye-tracking data into eye movements, such as fixations and saccades. Our method does not use prior assumptions about the relationship between years of experience and does not require the separation of eye-tracking data. Our best performing skill classification model achieves an F1 score of 98% and 70% for expert and trainee classes respectively. We also show that years of experience as a direct measure of skill, is significantly correlated to the expertise of a sonographer.

Applications of artificial intelligence in obstetrics

Artificial intelligence, which has been applied as an innovative technology in multiple fields of healthcare, analyzes large amounts of data to assist in disease prediction, prevention, and diagnosis, as well as in patient monitoring. In obstetrics, artificial intelligence has been actively applied and integrated into our daily medical practice. This review provides an overview of artificial intelligence systems currently used for obstetric diagnostic purposes, such as fetal cardiotocography, ultrasonography, and magnetic resonance imaging, and demonstrates how these methods have been developed and clinically applied.