Machine learning (ML) has revolutionized gastrointestinal (GI) endoscopy by improving diagnostic precision, reducing variability, and streamlining workflows. ML algorithms analyze endoscopic images or videos to detect patterns, identify lesions, and assist in diagnosing, characterizing, or localizing GI diseases such as polyps, cancers, inflammatory bowel disease, and ulcers.
Machine learning (ML) has revolutionized gastrointestinal (GI) endoscopy by improving diagnostic precision, reducing variability, and streamlining workflows. ML algorithms analyze endoscopic images or videos to detect patterns, identify lesions, and assist in diagnosing, characterizing, or localizing GI diseases such as polyps, cancers, inflammatory bowel disease, and ulcers. This integration enhances lesion detection rates, predicts disease progression, and standardizes examination quality, reducing operator-dependent variability.
Key applications of ML in GI endoscopy include:
1. **Computer-Assisted Detection (CADe):** Identifying lesions such as polyps or bleeding during endoscopy.
2. **Computer-Assisted Diagnosis (CADx):** Characterizing and classifying lesions to aid in diagnosis.
3. **Therapeutic Assistance:** Supporting interventional decisions, such as predicting lymph node metastasis (LNM) in colorectal cancer (CRC) to reduce unnecessary surgeries.
4. **Technical Support:** Enhancing procedural performance, such as scope insertion guidance for more precise and comfortable colonoscopies.
Despite these advancements, challenges remain. High-quality, diverse, and annotated training datasets are essential, but current datasets often lack diversity, contain biases, and underrepresent rare diseases. Manual annotation is time-consuming, and low-quality images or artefacts can hinder accuracy. Additionally, balancing model accuracy with speed, cost, and computational efficiency is crucial.
Ethical and regulatory concerns, such as patient privacy, algorithmic bias, data security, and transparency, are significant barriers to adoption. Real-time deployment is also limited by technical infrastructure, false positives, and workflow disruptions.
Future success will require standardized protocols, clinician training, interdisciplinary collaboration, and advancements in explainable AI to build trust and ensure safe, effective integration of ML into GI endoscopy.
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