A labeled dataset of simulated phlebotomy procedures for medical AI: polygon annotations for object detection and human-object interaction

2026-02-04

AI Summary

This work addresses the scarcity of high-quality, fine-grained annotated datasets for venipuncture procedures in medical AI research by introducing a publicly available dataset comprising 11,884 high-resolution images capturing the full simulated venipuncture workflow. The dataset features polygon-based annotations for five categories of medical objects and is partitioned into training, validation, and test sets in a 70/15/15 split. To enhance data utility and ensure privacy, the authors innovatively integrate SSIM-based redundancy reduction, automated facial anonymization, and YOLOv8-compatible segmentation formatting. As the first dataset to offer fine-grained, procedure-oriented annotations for venipuncture, it enables diverse downstream tasks such as instrument detection, procedural step recognition, and intelligent instructional feedback. The dataset has been released on Zenodo to advance research in medical AI and human-object interaction.

Technology Category

• Computer Vision: Segmentation
• Humans and AI: Interaction Techniques and Devices
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

This data article presents a dataset of 11,884 labeled images documenting a simulated blood extraction (phlebotomy) procedure performed on a training arm. Images were extracted from high-definition videos recorded under controlled conditions and curated to reduce redundancy using Structural Similarity Index Measure (SSIM) filtering. An automated face-anonymization step was applied to all videos prior to frame selection. Each image contains polygon annotations for five medically relevant classes: syringe, rubber band, disinfectant wipe, gloves, and training arm. The annotations were exported in a segmentation format compatible with modern object detection frameworks (e.g., YOLOv8), ensuring broad usability. This dataset is partitioned into training (70%), validation (15%), and test (15%) subsets and is designed to advance research in medical training automation and human-object interaction. It enables multiple applications, including phlebotomy tool detection, procedural step recognition, workflow analysis, conformance checking, and the development of educational systems that provide structured feedback to medical trainees. The data and accompanying label files are publicly available on Zenodo.

Problem

• phlebotomy
• medical AI
• object detection
• human-object interaction
• annotated dataset

Innovation

• polygon annotation
• SSIM-based frame filtering
• medical AI dataset
• human-object interaction
• phlebotomy simulation