Paper to Pixels: Enhancing Unilateral Neglect Assessment Using the New Computer Vision-Based Tool CANDO.

Background/

Objectives

The main aim of this article is to introduce a novel tool that allows the automatic scoring of many of the subtests from the conventional subpart of the Behavioural Inattention Test (BIT) and its German adaptation, the Neglect Test (NET). BIT and NET are standard test batteries used in the diagnosis of neglect. Our article has two parts. First, we examine the shortcomings of manual scoring, and secondly, we introduce our computer vision tool and evaluate its diagnostic validity and efficacy.

Methods

In Part 1, diagnostic consistency was examined across raters with varying expertise using selected BIT and NET tasks, with repeated assessments conducted under controlled evaluation conditions. In Part 2, a computer vision-based tool (CANDO) was developed to automate scoring using a deterministic computer vision pipeline designed to reproducibly apply scoring criteria across tasks. The performance of CANDO was compared with ground truth across cancellation, line bisection, and copying tasks.

Results

Manual scoring showed high overall agreement between and within raters under ideal conditions. However, diagnostic classification still differed across raters and repeated assessments. These inconsistencies were primarily driven by drawing and copying tasks. CANDO achieved very high accuracy for cancellation and line bisection tasks and strong agreement for copying tasks, while reducing variability associated with subjective judgment, time pressure, and oversight. The remaining discrepancies between computer vision and human raters had limited impact on diagnostic outcomes.

Conclusions

Manual assessment of unilateral neglect is vulnerable to inconsistencies arising from subjective evaluation and the structural limitations of scoring systems. Computer vision-based automation can reduce diagnostic variability, improve reproducibility, and increase assessment efficiency, while preserving clinically relevant information. The presented framework provides a practical tool to support higher-quality neglect assessment.