Shape Sensing-Based Documentation: Automatically Capture Every Moment

Imagine a surgeon completing a long, complex procedure and not having to write a single word of the operative report. Every maneuver, milestone, and complication has already been recorded and compiled. That’s the promise of shape sensing-based documentation—a future where reporting is accurate, complete, and effortless.

In this final blog of our series, “Every Flexible Medical Device Will Have Shape Sensing”, we explore how shape sensing provides the missing data to make automated documentation a reality for flexible tools.

The Problem with Today’s Documentation

Operative reports are often described as the single most important document in surgery, but they are notoriously tedious to create. Studies have shown that more than half of surgeon-written reports contain significant discrepancies, and critical details are often missing. The process is subjective, time-consuming, and inconsistent across operators.

In flexible endoscopy and vascular procedures, the gaps are even wider. A colonoscopy report may note that “looping occurred” or “scope reached the cecum,” but these statements are vague. In angiography, a physician might summarize which vessels were accessed but omit whether the guidewire prolapsed, kinked, or required repositioning. These omissions aren’t intentional—they happen because manual reporting can’t possibly capture the complexity of every bend, twist, or adjustment in real time.

Early Efforts at Automated Documentation

Automation has already begun to change this picture, particularly in rigid tool procedures:

• Robotic Surgery: Researchers have trained AI systems on videos of robotic surgeries to recognize surgical steps and auto-generate draft operative notes. In trials, these AI-generated reports were more accurate and complete than surgeon-written ones, catching details that physicians often missed.
• Endoscopy AI: Tools can automatically recognize insertion and instrument use during colonoscopies, logging each event and capturing images. Similarly, other systems flag polyps in real time, and its findings are now being integrated directly into electronic reports.
• Video and Imaging Integration: Some systems already use computer vision to measure features like the length of Barrett’s esophagus from video, or to timestamp when an anatomical landmark was reached.

These advances show that automated documentation is feasible. But they also highlight the limitation: most current systems capture what’s visible in the camera’s field of view or detectable via rigid robotic arms. They do not fully account for flexible tools inside the body.

The Blind Spot: Flexible Tools

Flexible devices—endoscopes, guidewires, and catheters—are central to modern minimally invasive medicine. Yet their behavior inside the body often escapes documentation.

• Endoscopy video shows the view from the tip, but not looping or torque along the shaft.
• Fluoroscopy provides 2D snapshots, but only intermittently, with radiation exposure.
• EM trackers at the tip tell you where the end is, but not what the rest of the device is doing.

Without full-shape information, we cannot reliably document events like looping, kinking, or prolapse. These events matter—not just clinically but also for procedure length, patient discomfort, and long-term outcomes. Automated documentation that ignores them is incomplete.

Shape Sensing: Making the Invisible Visible

Shape sensing closes this gap. By embedding a hair-thin optical fiber into a device, we can capture its full 3D geometry in real time, continuously.

This means automated systems can now:

• Detect when and where loops form in a colonoscope.
• Identify if a guidewire prolapses out of a vessel.
• Recognize torque build-up or buckling along a catheter.
• Confirm when a device has reached a target anatomy.

These events, once invisible, can be logged automatically. Instead of a vague “scope reached cecum,” the report could note the exact insertion depth, loop formation and resolution, and time of cecal intubation—all backed by objective data.

Shape sensing transforms a flexible device into a black box recorder of the procedure.

Adding Procedural Context

Shape data alone is powerful, but when paired with procedural context, it becomes transformative.

• Imaging Integration: Combining shape data with CT and/or fluoroscopy enables device tracking with an anatomy. This enables documentation that specifies which vessel was cannulated and when.
• Vision AI: Pairing shape sensing with systems that identify polyps can automatically document where in the colon and at what insertion distance they were found.
• Physiological Data: Adding vital signs or intraoperative events enriches the record further, tying device behavior to patient response.

In short, shape sensing provides the device coordinates, while contextual data provides the map. Together, they create a precise, timestamped log of the entire procedure.

Examples in Practice

• Gastrointestinal Endoscopy: Shape sensing can confirm scope advancement milestones (rectum, sigmoid, transverse, cecum), automatically document looping events, and precisely log the location of every polyp resection. In Barrett’s esophagus, AI systems already measure abnormal segments from video; shape data could add precise anatomical distances to that report.
• Vascular Procedures: Catheterization reports could automatically list every artery accessed, with timestamps and device usage. Shape sensing could also log complications like prolapse or kinking. Imagine a doctor finishing an angioplasty and receiving not just images but a 3D map of the catheter path, auto-generated as part of the record.

Why It Matters

Automated documentation powered by shape sensing isn’t just about convenience. It improves:

• Accuracy – Capturing details humans forget or overlook.
• Efficiency – Saving physicians time by turning them into editors, not authors, of reports.
• Transparency – Providing objective data for training, quality metrics, and audits.
• Safety – Offering real-time alerts when dangerous events (like severe kinking) occur.

And perhaps most importantly, it creates a procedural data library. Every automatically documented case feeds into a growing dataset that can inform AI guidance, tool development, and even future autonomous systems.

Looking Ahead

Automated documentation is the natural capstone of this series. Together, pre-case planning, AI-guided navigation, and autonomous procedures generate immense volumes of procedural data. Automated documentation ensures that none of it is lost.

Shape sensing makes this possible for flexible tools, transforming every bend and maneuver into a digital record.

This is the fourth and final post in our “Every Flexible Medical Device Will Have Shape Sensing” series:

1. Pre-Case Planning & Intelligent Tool Selection
2. AI-Guided Procedures
3. Autonomous Procedures
4. Automated Documentation (this post)

With these four pillars, we can see how shape sensing lays the foundation for the future of intelligent surgery—one where every step is guided, optimized, and documented.

Interested in leveraging full-length shape sensing for your next-generation medical devices? Contact us to explore collaboration opportunities.