Surgical Video Annotation

This task frames the scalpel’s dance—think “incision” tagged in a clip or “forceps” boxed in a shot (e.g., “suture” marked, “bleed” flagged)—to train AI to watch surgery like a pro. Our team annotates these moves, guiding tech to cut smarter and teach better.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are crucial in orchestrating the annotation and structuring of data for Surgical Video Annotation within healthcare AI workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to label datasets that enhance AI’s ability to recognize surgical elements accurately.

Training and Onboarding

PMs design and implement training programs to ensure workers master action tagging, instrument annotation, and anatomy labeling. For example, they might train teams to tag “clamp” in a video frame or mark “artery” in a procedure, guided by sample footage and medical standards. Onboarding includes hands-on tasks like annotating surgical clips, feedback loops, and calibration sessions to align outputs with AI surgical goals. PMs also establish workflows, such as multi-frame reviews for fast-paced actions.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., annotating 15,000 video frames) and set metrics like action accuracy, instrument precision, or anatomy consistency. They track progress via dashboards, address annotation errors, and refine methods based on worker insights or evolving surgical needs.

Collaboration with AI Teams

PMs connect annotators with machine learning engineers, translating technical requirements (e.g., high sensitivity for tiny tools) into actionable annotation tasks. They also manage timelines, ensuring labeled datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The annotators, taggers, or surgical analysts perform the detailed work of labeling and structuring video datasets for AI training. Their efforts are visual and clinical, requiring precision and medical expertise.

Labeling and Tagging

For surgical data, we might tag actions as “cut” or “stitch.” In complex tasks, they label specifics like “bone saw” or “vein retract.”

Contextual Analysis

Our team decodes footage, boxing “scalpel” in a slice or tagging “ligament” in a view, ensuring AI tracks every operating room beat.

Flagging Violations

Workers review datasets, flagging mislabels (e.g., “probe” as “needle”) or blurry frames (e.g., shaky cams), maintaining dataset quality and reliability.

Edge Case Resolution

We tackle complex cases—like obscured views or rare procedures—often requiring frame-by-frame analysis or escalation to surgical experts.

We can quickly adapt to and operate within our clients’ healthcare platforms, such as proprietary surgical tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of frames per shift, depending on the complexity of the videos and annotations.

Data Volumes Needed to Improve AI

The volume of annotated video data required to enhance AI systems varies based on the diversity of procedures and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional surgical model might require 5,000–20,000 annotated frames per category (e.g., 20,000 laparoscopic clips). For varied or rare surgeries, this could rise to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 frames per issue (e.g., missed instruments) are often needed. For instance, refining a model might demand 5,000 new annotations.

Scale for Robustness

Large-scale applications (e.g., multi-specialty robotics) require datasets in the hundreds of thousands to handle edge cases, unique actions, or new techniques. An annotation effort might start with 100,000 frames, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags tricky frames for further annotation. This reduces total volume but requires ongoing effort—perhaps 500–2,000 frames weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and surgical precision across datasets.

Multilingual & Multicultural Surgical Video Annotation

We can assist you with surgical video annotation across diverse linguistic and cultural landscapes.

Our team is equipped to label and analyze surgical data from global medical settings, ensuring accurate, contextually relevant datasets tailored to your specific AI objectives.

We work in the following languages: