Mobile Tissue Imaging for Real-Time Intraoperative Classification
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Solution Overview
Problem
Existing technologies face challenges in accurately and efficiently identifying and categorizing tissue during surgery, particularly in providing timely and accurate pathologist, which can be used to determine whether a pathologist's determination that the image is normal or abnormal. The pathologist's analysis of tissue during surgery, while ensuring that the appropriate tissues (e.g., tumorous, cancerous, etc.) are excised, while other tissues (e.g., normal, healthy) are not inadvertently excised. In conventional practice, a tissue specimen is removed from a patient during surgery and is then examined by a pathologist who determines that the tissue is normal or abnormal. The pathologist typically operates from a pathology lab or department of a hospital, which can be located away from the operating room where the surgery occurs, and may not be present during a time period when a surgeon needs a tissue specimen analyzed (i.e., when the patient is in surgery).
Innovation Solution
The systems and methods disclosed herein utilize a mobile device to analyze an image of a tissue sample using a Stimulated Raman Histology (SRH) image of a tissue sample, the system can provide a surgeon or medical professional with an indication that the tissue is normal or abnormal within a short period of time without requiring time-consuming analysis by a pathologist, thereby reducing the risk to a patient. In some embodiments, the system can analyze the SRH image can be displayed on a display device of the Raman Spectroscopy device to provide an accurate image of a tissue sample. The system can include a neural network circuit 145 can be configured to perform a tissue analysis circuit 145 can be configured to differentiate between a normal tissue and an abnormal tissue, and can generate a tissue classification that classifies the tissue as normal or abnormal. In another embodiment, the mobile device 100 can distinguish one tissue from another tissue. In yet another embodiment, the mobile device 100 can be used to determine a characteristic of a tissue during a surgical operation (e.g., a tumor removal procedure).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a pathologist manually analyzes tissue specimens in a pathology lab, then accurate tissue classification is achieved, but time consumption increases and real-time intraoperative decision-making is delayed
Solution Approach 1:
The system creates a digital copy of the tissue specimen through imaging (photography or digital scanning) and analyzes this copy using computer vision and machine learning algorithms. This allows the tissue to be examined without requiring physical presence of a pathologist in the operating room, thereby reducing time loss while maintaining classification accuracy through automated neural network analysis of the image copy.
Solution Approach 2:
The manual mechanical process of pathologist examination is replaced with an automated electronic system comprising image capture devices, processing circuits, and machine learning models. The neural network automatically classifies tissue as normal or abnormal, substituting the mechanical human examination process with an electronic automated system that operates rapidly without requiring pathologist presence in the operating room.
2Speed
If a pathologist is present in the operating room for real-time tissue analysis, then response time is reduced, but device complexity and operational logistics increase
Solution Approach 1:
The mobile device serves multiple functions: it captures images of tissue specimens, processes these images through neural networks, stores image data, and displays classification results. This multi-functional mobile device replaces the need for specialized pathology lab infrastructure and pathologist presence, achieving rapid tissue analysis through a universal device already present in the operating room.
Solution Approach 2:
The system enables self-service tissue analysis where the mobile device autonomously captures images, performs neural network-based classification, and provides results without requiring external pathologist intervention. The automated system serves itself by integrating image capture, processing, and decision-making capabilities within the operating room environment, eliminating the need for complex pathology lab infrastructure.
3Reliability
If traditional pathology lab facilities are used for tissue analysis, then comprehensive examination capabilities are available, but ease of operation and accessibility during surgery are reduced
Solution Approach 1:
The system transitions from three-dimensional physical tissue examination in a pathology lab to two-dimensional digital image analysis on a mobile device. By converting the tissue specimen into an image format, the system enables examination anywhere in the operating room without requiring physical transport to a pathology lab, thereby improving intraoperative accessibility while maintaining reliable examination capabilities through digital imaging and automated analysis.
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
The system provides a surgeon with an indication that the tissue is normal or abnormal within a short period of time without requiring time-consuming analysis by a pathologist, thereby reducing the risk to a patient.
Implementation Method 1
capturing, by an optical reader device of a mobile device, an image of a tissue
Implementation Method 2
The neural network can be a pretrained neural network that is trained to classify the image of the tissue as normal or abnormal
Data Source
AI summary
Presented herein are systems and methods relating to artificial intelligence-drive intraoperative diagnosis. For example, a method can include capturing, by an optical reader device of a mobile device, an image of a tissue. A method can further include providing, by a mobile application of the mobile device, the image of the tissue to a tissue analysis circuit. A method can include receiving, from the tissue analysis circuit via the mobile device, a tissue classification. A method can include presenting, via a graphical user interface of the mobile device, a display screen comprising the tissue classification.