Intelligent Surgical Marker for Lesion Margin Delineation
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Solution Overview
Problem
Current medical imaging modalities, such as X-ray CT, MRI, and ultrasound, lack sufficient spatial resolution to reveal microscopic morphological features associated with skin pathology, making it challenging to accurately guide surgical procedures for skin cancers like nonmelanoma skin cancers (NMSCs).
Innovation Solution
The development of an optical coherence tomography (OCT) integrated surgical guidance platform that includes a handheld probe device capable of capturing OCT images, segmenting tissue types using a neural network, and determining lesion margins with a one-class classifier, while simultaneously creating visible labels on the lesion margins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional medical imaging modalities (X-ray CT, MRI, ultrasound) are used to guide surgery, then the overall surgical procedure can be planned, but the spatial resolution is insufficient to reveal microscopic morphological features of skin pathology
Solution Approach 1:
The patent replaces conventional mechanical imaging modalities (X-ray CT, MRI, ultrasound) with optical coherence tomography (OCT), which uses optical waves instead of mechanical or electromagnetic radiation. This substitution enables microscopic resolution of skin tissue structures, revealing morphological features that are invisible to conventional modalities, thereby resolving the contradiction between resolution and reliability.
Solution Approach 2:
The patent changes the imaging parameter from macroscopic/mesoscale resolution to microscopic resolution by utilizing optical waves with wavelengths appropriate for tissue microstructure imaging. This parameter change in the imaging modality enables the system to detect and delineate lesion margins at the microscopic level, simultaneously improving both spatial resolution and the reliability of surgical guidance.
2Reliability
If Mohs micrographic surgery is performed to achieve high cure rates for NMSCs, then the cure rate is maximized, but the surgical procedure takes one to two hours or longer
Solution Approach 1:
The patent performs preliminary action by using OCT imaging and machine learning algorithms to pre-identify and mark the precise location of lesion margins before the surgical excision begins. The system processes OCT images, segments tissue types, detects boundaries, and creates visible labels on the skin surface in advance, allowing the surgeon to directly target and remove only the affected tissue without time-consuming intraoperative examination.
Solution Approach 2:
The patent creates a digital copy of the skin tissue structure through OCT imaging, which captures the microscopic morphology of the lesion and surrounding tissue. This digital replica is then processed by machine learning models to identify margins, eliminating the need for repeated physical excision and examination cycles, thereby reducing surgical time while maintaining high cure rates.
3Reliability
If multiple tissue excision stages are used in Mohs micrographic surgery to address subsurface malignancy, then complete tumor removal is achieved, but the time required increases significantly
Solution Approach 1:
The patent implements feedback by using OCT imaging to provide real-time information about tissue structure and malignancy during the surgical process. The machine learning system continuously analyzes OCT data, identifies tumor boundaries, and guides excision in real-time, eliminating the need for multiple sequential excision stages and allowing complete tumor removal in a single efficient procedure.
Solution Approach 2:
The patent integrates multiple functions into a single system: OCT imaging for microscopic visualization, machine learning for automatic margin detection, and optical marking for surgical guidance. This multi-functional integration enables complete tumor removal without requiring separate excision stages, thereby improving surgical efficiency while maintaining reliability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables accurate and objective delineation of lesion margins, improving the precision of surgical excisions and reducing the time required for procedures like Mohs micrographic surgery, while also overcoming challenges related to speckle noise and depth-dependent signal decay in OCT images.
Implementation Method 1
a fiber-optic probe assembly configured to direct low-coherence light to a region of interest and collect light reflected from the region of interest to acquire the OCT image(s)
Data Source
AI summary
Surgical marker systems and methods for delineating a lesion margin of a subject are provided. An example system includes a handheld probe device configured to capture an optical coherence tomography (OCT) image and a processor coupled to a memory. The handheld probe device includes a handheld probe including a fiber-optic probe assembly and a marker assembly. The processor is configured to: segment, by a neural network, each pixels of the OCT into different tissue-type categories; generate one or more feature vectors based at least in part on the segmented pixels; determine, by a one-class classifier, a boundary location in the OCT image between a normal tissue and an abnormal tissue of the tissue structure; and control the marker assembly to selectively create a visible label on a tissue location of the subject, the tissue location corresponding to the boundary location.