Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

Wong, Alex Ngai Nick; He, Zebang; Leung, Ka Long; To, Curtis Chun Kit; Wong, Chun Yin; Wong, Sze Chuen Cesar; Yoo, Jung Sun; Chan, Cheong Kin Ronald; Chan, Angela Z.; Lacambra, Maribel D.; Yeung, Martin Ho Yin

doi:10.3390/cancers14153780

Cited by 24 publications

(15 citation statements)

References 146 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Te algorithm works by distributing varying proportions to represent each colour. Any two colours in two diferent histogram bins are diferent even if they are similar, and each histogram bin has similar colours even if the colours difer [51]. Ten, the algorithm checks the membership of each colour and extracts the colour features.…”

Section: A Hybrid Approach Based On Integrating Features Of Cnn Withmentioning

confidence: 99%

Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features

Al-Mekhlafi

Senan

Alshudukhi

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

Gastrointestinal (GI) diseases, particularly tumours, are considered one of the most widespread and dangerous diseases and thus need timely health care for early detection to reduce deaths. Endoscopy technology is an effective technique for diagnosing GI diseases, thus producing a video containing thousands of frames. However, it is difficult to analyse all the images by a gastroenterologist, and it takes a long time to keep track of all the frames. Thus, artificial intelligence systems provide solutions to this challenge by analysing thousands of images with high speed and effective accuracy. Hence, systems with different methodologies are developed in this work. The first methodology for diagnosing endoscopy images of GI diseases is by using VGG-16 + SVM and DenseNet-121 + SVM. The second methodology for diagnosing endoscopy images of gastrointestinal diseases by artificial neural network (ANN) is based on fused features between VGG-16 and DenseNet-121 before and after high-dimensionality reduction by the principal component analysis (PCA). The third methodology is by ANN and is based on the fused features between VGG-16 and handcrafted features and features fused between DenseNet-121 and the handcrafted features. Herein, handcrafted features combine the features of gray level cooccurrence matrix (GLCM), discrete wavelet transform (DWT), fuzzy colour histogram (FCH), and local binary pattern (LBP) methods. All systems achieved promising results for diagnosing endoscopy images of the gastroenterology data set. The ANN network reached an accuracy, sensitivity, precision, specificity, and an AUC of 98.9%, 98.70%, 98.94%, 99.69%, and 99.51%, respectively, based on fused features of the VGG-16 and the handcrafted.

show abstract

Section: A Hybrid Approach Based On Integrating Features Of Cnn Withmentioning

confidence: 99%

Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features

Al-Mekhlafi

Senan

Alshudukhi

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…However, this is a time-consuming process. Further, there is a constant increase in the number of samples that are sent for analysis to the anatomical pathological laboratory and this, coupled with insufficient skilled pathologists, leads to long turn-around-times [ 125 ]. Additionally, cytopathology requires the accurate slide preparation and optimal staining of the tissue slices.…”

Section: Ai-driven Diagnosis Based On Cancer Biomarkersmentioning

confidence: 99%

“…Additionally, cytopathology requires the accurate slide preparation and optimal staining of the tissue slices. However, the staining intensity of biopsy slides exhibit analyst-based, sample thickness-based and laboratory protocol-based variations in the intensity [ 125 ]. In this context, deep learning algorithms, such as VGG, DenseNet, ResNet etc., and machine learning algorithms, based on SVM and the random forest, can be employed to extract specific tumour features from the tissue slices to improve the speed of detection and reduce the burden on the clinical pathologists.…”

Section: Ai-driven Diagnosis Based On Cancer Biomarkersmentioning

confidence: 99%

“…Abnormal goblet cells have been identified with an 86% accuracy in tissue samples of individuals with Barrett’s esophagus, using VGG algorithms [ 130 ]. AI-driven algorithms can be useful in detecting microsatellite instabilities in the biopsy samples, that area hallmark of many forms of cancers [ 125 ]. These studies clearly demonstrate that the integration of machine learning in cytopathology can be useful for the faster, efficient, and early diagnosis of pancreatic cancer.…”

Section: Ai-driven Diagnosis Based On Cancer Biomarkersmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Hameed

Krishnan

2022

Cancers

View full text Add to dashboard Cite

Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians’ treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.

show abstract

“…gastrointestinal cancers [2]. Wong et al provided an overview of the current histological practices in anatomical pathology laboratories with respect to challenges faced in image preprocessing, presented the existing AI-based algorithms, discussed their limitations and presented clinical insight with respect to the application of AI in the early detection and diagnosis of gastrointestinal cancer.…”

mentioning

confidence: 99%