AI-assisted endoscopy is revolutionizing healthcare by enhancing diagnostic accuracy and reducing procedure times. This technology enables real-time analysis, improving patient outcomes.

Endoscopy has existed for many years, but new technology is improving it. One of the most exciting advancements is using artificial intelligence (AI) to assist doctors during endoscopy. From diagnosis to surgery, AI-assisted endoscopy is reshaping healthcare outcomes. 

Modern technologies are enhancing the capabilities of AI more frequently. As AI evolves, its use in endoscopy is expected to grow, revolutionizing medical practices even in endoscopy reporting. The advanced endoscopy reporting system has become the best companion of AI endoscopy. 

What else? Let’s explore the cutting-edge world of AI endoscopy and how it’s transforming how we detect and treat conditions.

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What is AI-assisted endoscopy?

AI-powered endoscopy combines the power of AI with traditional endoscopy. AI is a type of computer technology that can learn and make decisions. In healthcare, AI can analyze images and data quickly and accurately.

During an endoscopy, the AI system analyzes the video from the endoscope in real-time. It helps doctors spot abnormalities, like polyps or tumors, that might be hard to see with the naked eye. The advent of artificial intelligence in colonoscopy has dramatically improved the detection of adenomas and polyps. AI helps find polyps that are often missed by traditional methods. This improvement reduces the miss rate for adenomas and polyps by up to 50%.

Key Technologies in AI-powered endoscopy

The core components of AI in endoscopic solutions include computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems. These technologies use machine learning, especially deep learning, to analyze endoscopic images and videos in real-time. This helps detect and characterize different anomalies of the GI tract.

Computer-Aided Detection (CADe)

Computer-Aided Detection (CADe) systems are designed to assist endoscopists by automatically identifying potential abnormalities during endoscopic examinations. These systems use advanced machine learning algorithms to analyze endoscopic images and videos in real time. They identify and highlight areas that may need more investigation.

Function: Detection of Abnormalities

CADe systems enhance the endoscopist’s ability to detect abnormalities such as polyps, lesions, and early signs of cancer. By providing a second set of eyes, these systems reduce the likelihood of missed diagnoses and improve overall detection rates.

Example: Detection of Colorectal Polyps

In the context of colorectal cancer screening, CADe systems have demonstrated a significant impact. During colonoscopies, CADe can highlight polyps that the human eye might overlook. This technology has increased polyp detection rates, which is crucial for early intervention and prevention of colorectal cancer.

Computer-Aided Diagnosis (CADx)

Computer-aided diagnosis (CADx) systems further detect abnormalities and assist in their characterization and diagnosis. These systems analyze detected anomalies and assess their nature to distinguish benign and malignant lesions.

Function: Characterization and Diagnosis

CADx systems utilize deep learning models trained on vast annotated image datasets to provide diagnostic insights. They help endoscopists make more informed decisions by offering real-time evaluations of the detected abnormalities.

Example: Differentiating Between Benign and Malignant Tumors

In gastrointestinal endoscopy, CADx systems can differentiate between benign growths and malignant tumors. During an esophagogastroduodenoscopy (EGD), CADx can assist in determining if a lesion in the esophagus is benign or cancerous. This aids the endoscopist in making important diagnostic and treatment choices.

One real-life example of AI-powered endoscopy technology is CAD EYE by Fujifilm.

Fujifilm’s CAD Eye exemplifies the integration of both CADe and CADx technologies, providing a comprehensive AI-assisted solution for endoscopic procedures. CAD Eye is designed to enhance the detection and diagnosis of colorectal polyps during colonoscopy, offering real-time support to endoscopists.

Function: Real-Time Detection and Diagnosis

CAD Eye analyzes the endoscope’s live video feed, detecting potential polyps and providing diagnostic suggestions. The system helps identify suspicious areas and provides probability-based assessments, allowing endoscopists to make informed decisions quickly.

A Real-Life Example: CAD EYE by Fujifilm

CAD EYE is an AI system developed by Fujifilm to assist with endoscopies. CAD stands for Computer-Aided Detection. CAD EYE is designed to help doctors detect and characterize polyps in the colon. 

Here’s how CAD EYE works:

  1. Real-Time Analysis: CAD EYE analyzes the video from the endoscope as the doctor performs the procedure. With white light and LCI (Linked Color Imaging) mode, doctors can detect polyps and flat lesions using CAD EYE. 
  2. Detection: It highlights areas with polyps; the screen will indicate specific areas requiring the doctor’s attention through detection boxes, visual assist circles, or visual sounds.

This system, when used in LCI mode, detected every lesion with 100% sensitivity. It also had a very low rate of false positives.

  1. Characterization: Characterization CAD EYE also helps identify the type of polyp. The mapping with the color code in the corner of the screen shows whether the polyp is hyperplastic or neoplastic. This is important because some polyps are more likely to turn into cancer than others.
  2. Feedback: The system provides immediate feedback, allowing the doctor to make informed decisions on the spot.

Benefits of AI in Endoscopic Solutions

A recent report shows that the global market for AI in endoscopy is expected to reach $738.9 million by 2031. The reason is that AI-assisted endoscopy offers several benefits that are transforming healthcare. Let’s look at some of these benefits in detail.

1. Improved Detection Rates

One of the most significant advantages of AI-powered endoscopy is improved detection rates. Even experienced doctors can miss small or flat polyps. AI systems like CAD EYE can analyze images with incredible accuracy and consistency. This means that more polyps are detected early, which can prevent them from turning into cancer.

2. Enhanced Accuracy

AI systems can differentiate between different types of polyps. This assists doctors in determining the most appropriate course of action. For example, if a polyp is likely benign, the doctor might decide to monitor it. If it looks suspicious, the doctor can remove it immediately. This precision reduces unnecessary procedures and focuses treatment where it is most needed.

3. Reduced Procedure Time

With AI assistance, doctors can perform endoscopies more efficiently. The AI system quickly highlights areas of concern, reducing the time spent searching for abnormalities. This can make the procedure faster and less uncomfortable for patients.

4. Lower Costs

Early detection of polyps and other issues can lead to less invasive and less expensive treatment than treating advanced diseases. Implementing AI in GI endoscopy can reduce healthcare expenses by enhancing detection rates.

5. Better Patient Outcomes

AI-assisted endoscopy leads to better patient outcomes. Detecting polyps before they turn into cancer means that patients have a better chance of a full recovery. AI in gastrointestinal endoscopy reduces the risk of missing essential findings, which helps with prompt and effective treatment.

Challenges 

While cutting-edge AI technologies transform endoscopy, there are challenges and considerations for its widespread adoption and optimization.

  • Integration with Existing Systems

Ensuring seamless integration of AI technologies like CAD Eye with current endoscopic equipment and workflows is essential. This requires collaboration between technology developers and healthcare providers to address compatibility issues and streamline the adoption process.

  • Training and Education

Effective use of Artificial Intelligence in gastrointestinal endoscopy tools requires proper training and education for endoscopists. Continuous education and hands-on training are crucial to ensure clinicians can fully leverage the capabilities of these advanced systems.

  • Cost and Accessibility

The cost of implementing AI in endoscopic solutions can be a barrier for some healthcare providers, particularly in resource-limited settings. We should work to make affordable solutions and equal access to advanced technologies available to a broader group of patients.

Conclusion

The AI-assisted endoscopy is ushering in a new era of precision medicine and preventative care. Computer-aided detection and diagnosis technologies help endoscopists identify and analyze abnormalities more effectively. This leads to earlier interventions and improves patient outcomes. While challenges around integration, training, and ethical considerations remain, the immense potential of AI in endoscopy to save lives is undeniable.

As these new systems become more common, we expect fewer diagnostic mistakes, lower healthcare costs, and better detection of diseases like colorectal cancer in their early stages. The endoscopy revolution has arrived – and AI is at the helm.

Key Takeaways:

  1. AI-assisted endoscopy combines artificial intelligence with traditional endoscopic imaging to improve the detection and diagnosis of abnormalities like polyps and tumor AI powered endoscopy uses artificial intelligence with conventional endoscopic imaging to better detect and diagnose issues such as polyps and tumors.
  2. Computer-aided detection (CADe) systems automatically identify potential abnormalities, while computer-aided diagnosis (CADx) further characterizes them as benign or malignant.
  3. Fujifilm’s CAD EYE is one example that provides real-time polyp detection, highlighting, and characterization feedback to Endoscopists during procedures.
  4. Key benefits include higher polyp/lesion detection rates, enhanced diagnostic accuracy, reduced procedure times, lower costs, and improved patient outcomes.
  5. While promising, challenges exist around integrating existing systems, training/education for clinicians, and ensuring equitable access.
  6. As technology advances, AI endoscopy could revolutionize preventative screening and early intervention for conditions like colorectal cancer.
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Tahmin Aysha Murshed

Hi there! I am a professional content writer specialized in writing in Healthcare technology. As a computer science grad I… See More