ArticleCancers2021
Feedforward Artificial Neural Network-Based Colorectal Cancer Detection Using Hyperspectral Imaging: A Step towards Automatic Optical Biopsy.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it, 86 citations in OpenAlex.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Classification of esophageal cancer by using hyperspectral data.International journal of computer assisted radiology and surgery · 2026Article
- From Image-Guided Surgery to Computer-Assisted Real-Time Diagnosis with Hyperspectral and Multispectral Imaging: A Systematic Review in Gynecologic Oncology.Diagnostics (Basel, Switzerland) · 2026Review
- Autofluorescence and deep learning in early disease detection: biological foundations, clinical applications, and future directions.Frontiers in artificial intelligence · 2026Review
- From Innovation to Application: Can Emerging Imaging Techniques Transform Breast Cancer Diagnosis?Diagnostics (Basel, Switzerland) · 2025Review
- Artificial neural networks as a prognostic tool using hyperspectral imaging on pretherapeutic histopathological specimens of esophageal adenocarcinoma.Journal of cancer research and clinical oncology · 2025Article
- Exploring the role of preprocessing combinations in hyperspectral imaging for deep learning colorectal cancer detection.Scientific reports · 2025Article
- Accurate colorectal cancer detection using a random hinge exponential distribution coupled attention network on pathological images.Abdominal radiology (New York) · 2025Article
- Identification of colorectal malignancies enabled by phasor-based autofluorescence lifetime macroimaging and ensemble learning.Biophotonics discovery · 2025Article
- Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis.Journal of imaging · 2025Article
- Detection of flap malperfusion after microsurgical tissue reconstruction using hyperspectral imaging and machine learning.Scientific reports · 2025Article
- Toward real-time margin assessment in breast-conserving surgery with hyperspectral imaging.Scientific reports · 2025Article
- Improving lung cancer pathological hyperspectral diagnosis through cell-level annotation refinement.Scientific reports · 2025Article
- Masked image modeling in medical hyperspectral imaging: reconstruction evaluation and downstream tasks.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer.Biomedicines · 2024Review
- Distinguishing of Histopathological Staging Features of H-E Stained Human cSCC by Microscopical Multispectral Imaging.Biosensors · 2024Article
- Hyperspectral imaging facilitating resect-and-discard strategy through artificial intelligence-assisted diagnosis of colorectal polyps: A pilot study.Cancer medicine · 2024Article
- Surgical optomics: hyperspectral imaging and deep learning towards precision intraoperative automatic tissue recognition-results from the EX-MACHYNA trial.Surgical endoscopy · 2024Article
- Hyperspectral Imaging Detects Clitoral Vascular Issues in Gender-Affirming Surgery.Diagnostics (Basel, Switzerland) · 2024Article
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8 authors at 3 institutions in 2 countries.
Funding
Abstract
Currently, colorectal cancer (CRC) is mainly identified via a visual assessment during colonoscopy, increasingly used artificial intelligence algorithms, or surgery. Subsequently, CRC is confirmed through a histopathological examination by a pathologist. Hyperspectral imaging (HSI), a non-invasive optical imaging technology, has shown promising results in the medical field. In the current study, we combined HSI with several artificial intelligence algorithms to discriminate CRC. Between July 2019 and May 2020, 54 consecutive patients undergoing colorectal resections for CRC were included. The tumor was imaged from the mucosal side with a hyperspectral camera. The image annotations were classified into three groups (cancer, CA; adenomatous margin around the central tumor, AD; and healthy mucosa, HM). Classification and visualization were performed based on a four-layer perceptron neural network. Based on a neural network, the classification of CA or AD resulted in a sensitivity of 86% and a specificity of 95%, by means of leave-one-patient-out cross-validation. Additionally, significant differences in terms of perfusion parameters (e.g., oxygen saturation) related to tumor staging and neoadjuvant therapy were observed. Hyperspectral imaging combined with automatic classification can be used to differentiate between CRC and healthy mucosa. Additionally, the biological changes induced by chemotherapy to the tissue are detectable with HSI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.