Workflow impact of an automated prototype for measurements of angles and lengths in whole lower limb Hip-Knee-Ankle (HKA) Radiographs.

This prospective clinical research study aims to evaluate the impact of an artificial intelligence (AI)–based automated measurement tool on the workflow of pre-operative assessment in patients undergoing knee replacement surgery. The tool is designed to automatically calculate multiple angular and linear measurements from standing whole lower-limb (hip–knee–ankle) radiographs, which are essential for surgical planning.

Currently, these measurements are performed manually by radiologists or orthopaedic surgeons, a process that is time-consuming, subject to inter-observer variability, and dependent on operator expertise. The AI tool integrates computer vision techniques to provide consistent, reproducible measurements while allowing a “human-in-the-loop” for review, modification, and final approval.

The study will be conducted at Max Hospitals and will include adult patients undergoing pre-operative full lower-limb radiographs for knee arthroplasty. The primary outcome is the comparison of time taken for manual versus AI-assisted measurements. Secondary outcomes include assessment of workflow improvement, usability, and representation of measurements for orthopaedic surgeons.

The study aims to demonstrate improved efficiency, consistency, and clinical workflow integration of AI-assisted measurements in routine orthopaedic practice.