Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning.
George A StanciuKarsten KönigYoung Min SongLior WolfCostas A CharitidisPaolo BianchiniMartin GoetzPublished in: Biophysics reviews (2023)
According to the World Health Organization, the proportion of the world's population over 60 years will approximately double by 2050. This progressive increase in the elderly population will lead to a dramatic growth of age-related diseases, resulting in tremendous pressure on the sustainability of healthcare systems globally. In this context, finding more efficient ways to address cancers, a set of diseases whose incidence is correlated with age, is of utmost importance. Prevention of cancers to decrease morbidity relies on the identification of precursor lesions before the onset of the disease, or at least diagnosis at an early stage. In this article, after briefly discussing some of the most prominent endoscopic approaches for gastric cancer diagnostics, we review relevant progress in three emerging technologies that have significant potential to play pivotal roles in next-generation endoscopy systems: biomimetic vision (with special focus on compound eye cameras), non-linear optical microscopies, and Deep Learning. Such systems are urgently needed to enhance the three major steps required for the successful diagnostics of gastrointestinal cancers: detection, characterization, and confirmation of suspicious lesions. In the final part, we discuss challenges that lie en route to translating these technologies to next-generation endoscopes that could enhance gastrointestinal imaging, and depict a possible configuration of a system capable of (i) biomimetic endoscopic vision enabling easier detection of lesions, (ii) label-free in vivo tissue characterization, and (iii) intelligently automated gastrointestinal cancer diagnostic.
Keyphrases
- label free
- deep learning
- high resolution
- early stage
- healthcare
- machine learning
- high speed
- ultrasound guided
- multiple sclerosis
- artificial intelligence
- high throughput
- convolutional neural network
- mass spectrometry
- squamous cell carcinoma
- single molecule
- tissue engineering
- loop mediated isothermal amplification
- papillary thyroid
- radiation therapy
- childhood cancer
- climate change
- photodynamic therapy
- fluorescence imaging
- human health
- fine needle aspiration
- squamous cell
- middle aged
- lymph node metastasis
- small bowel
- sentinel lymph node