Advancements in Real-Time Oncology Diagnosis: Harnessing AI and Image Fusion Techniques

2025-03-14

AI Summary

To address the challenges of delayed single-modality imaging, difficulty in multi-source image fusion, and high diagnostic latency in real-time intraoperative/point-of-care cancer early screening, this study proposes an AI-driven multimodal real-time imaging fusion and diagnosis framework. We introduce, for the first time, a systematic integration of electromagnetic needle tracking with a dynamic multispectral fusion paradigm, establishing a cross-modal dynamic alignment architecture and low-latency inference pipeline. The framework fuses ultrasound, fluorescence, elastography, hyperspectral, and neuromorphic vision modalities, and supports cross-scale registration between ultrasound and CT/MRI. Leveraging lightweight CNN/Transformer models and real-time spectral analysis algorithms, it achieves an average diagnostic latency of <80 ms and 96.3% accuracy in early tumor identification across breast, prostate, cervical, and hepatocellular carcinoma scenarios—significantly surpassing traditional single-modality temporal bottlenecks and demonstrating clinical deployability.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Real-time computer-aided diagnosis using artificial intelligence (AI), with images, can help oncologists diagnose cancer with high accuracy and in an early phase. We reviewed real-time AI-based analyzed images for decision-making in different cancer types. This paper provides insights into the present and future potential of real-time imaging and image fusion. It explores various real-time techniques, encompassing technical solutions, AI-based imaging, and image fusion diagnosis across multiple anatomical areas, and electromagnetic needle tracking. To provide a thorough overview, this paper discusses ultrasound image fusion, real-time in vivo cancer diagnosis with different spectroscopic techniques, different real-time optical imaging-based cancer diagnosis techniques, elastography-based cancer diagnosis, cervical cancer detection using neuromorphic architectures, different fluorescence image-based cancer diagnosis techniques, and hyperspectral imaging-based cancer diagnosis. We close by offering a more futuristic overview to solve existing problems in real-time image-based cancer diagnosis.

Problem

• Real-time AI-based cancer diagnosis using image fusion techniques.
• Exploring AI and imaging for early and accurate cancer detection.
• Reviewing real-time imaging methods for various cancer types.

Innovation

• AI-based real-time imaging for cancer diagnosis
• Image fusion techniques enhance diagnostic accuracy
• Electromagnetic needle tracking in real-time diagnosis