SMART Bun-Yo-Matic X-Ray

The SMART Bun-Yo-Matic X-RAY software is an assistive tool for orthopaedic professionals, designed for diagnosing and planning surgical interventions for foot and ankle conditions, notably hallux valgus. It processes patient X-rays using machine learning to generate 3D anatomical axes, measurements, and visualization reports, thereby enabling precise pre-operative planning and assessment of potential corrections.

The SMART Bun-Yo-Matic X-RAY software is designed for orthopaedic healthcare professionals to aid in the diagnosis and surgical planning of foot and ankle conditions. It utilizes X-ray images as input to generate visualization reports of three-dimensional mathematical models of foot and ankle anatomy and orthopaedic fixation devices, provides measurement templates with radiographic measures, and offers a surgical planning application. This application allows for the visualization of anatomical 3D structures, radiographic measures, and surgical instrument parameters, facilitating the planning of treatments and operations, particularly for conditions like hallux valgus. The device's methodology involves processing X-rays of the foot to produce 3D axes on contextual bone models. It employs machine learning algorithms for bone and metal identification; the bone identification algorithm was developed using 1,5776 X-ray and Cone Beam Computed Tomography (CBCT) Digitally Reconstructed Radiographs (DRR), and metal identification was developed using 15 such images. Testing was performed on 97 images from diverse patient demographics and imaging systems. Performance testing confirmed its accuracy, meeting criteria such as 95% model conformance within 1.0mm distance to a reference model and 2.0 degrees standard deviation for angular measurements. Surgical planning accuracy was within ±1 degree for angular and ±1.0 mm for distance measurements, with tests including AI/ML validation, comparison to manual measurements, and evaluation of planned versus actual surgical correction, all showing the device performed as intended.

AI Regulatory Concerns

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Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. How does the SMART Bun-Yo-Matic X-RAY software's performance in generating 3D axes and surgical plans vary when using plain-film native X-ray images versus Cone Beam Computed Tomography (CBCT) Digitally Reconstructed Radiographs (DRR) as input?; What specific types of orthopaedic fixation devices are included in the software's three-dimensional models, and how extensible is this library for new or custom devices?; The AI algorithm for bone identification was developed using 1,5776 images; what was the diversity of foot pathologies (beyond hallux valgus) within this training set, and how does that affect its applicability to less common conditions?; How does the 'contextual bone model' used by the SMART Bun-Yo-Matic X-RAY adapt to significant anatomical variations or deformities beyond typical hallux valgus presentations?; What were the specific outcomes or metrics used to compare the 'planned surgical correction to the actual surgical correction' during performance testing, and what was the observed range of deviation?