Development and Validation of an Automatic Ultrawide-Field Fundus Imaging Enhancement System for Facilitating Clinical Diagnosis:A Cross-Sectional Multicenter Study

In ophthalmology,the quality of fundus images is critical for accurate diagnosis,both in clinical practice and in artificial intelligence(AI)-assisted diagnostics.Despite the broad view provided by ultrawide-field(UWF)imaging,pseudocolor images may conceal critical lesions necessary for precise diagnosis.To address this,we introduce UWF-Net,a sophisticated image enhancement algorithm that takes disease characteristics into consideration.Using the Fudan University ultra-wide-field image(FDUWI)dataset,which includes 11294 Optos pseudocolor and 2415 Zeiss true-color UWF images,each of which is rigorously annotated,UWF-Net combines global style modeling with feature-level lesion enhancement.Pathological consistency loss is also applied to maintain fundus feature integrity,significantly improving image quality.Quantitative and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization(CLAHE)and structure and illumination constrained generative adversarial network(StillGAN),delivering superior retinal image quality,higher quality scores,and preserved feature details after enhancement.In disease classification tasks,images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by StillGAN,demonstrating a 4.62%increase in sensitivity(SEN)and a 3.97%increase in accuracy(ACC).In a multicenter clinical setting,UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors,and yielded a significant reduction in diagnostic time((13.17±8.40)s for UWF-Net enhanced images vs(19.54±12.40)s for original images)and an increase in diagnostic accuracy(87.71%for UWF-Net enhanced images vs 80.40%for original images).Our research verifies that UWF-Net markedly improves the quality of UWF imaging,facilitating better clinical outcomes and more reliable AI-assisted disease classification.The clinical integration of UWF-Net holds great promise for enhancing diagnostic processes and patient care in ophthalmology.

Artificial intelligence assisted endocytoscopy: A novel eye in endoscopy

Over the past few years,emerging new approaches in endoscopic imaging technologies facilitate a high-quality assessment of lesions found in the gastrointestinal(GI)tract.Endocytoscopy(EC),as a novel tool in endoscopy,aids the more accurate evaluation of superficial mucosal surface.This review article aims to represent the most relevant information related to the latest EC technology and its clinical application in the lower GI tract diagnostic.We discuss EC-computer-aided diagnosis capability to differentiate between non-neoplastic and neoplastic lesion that offers a closer look to in-vivo assessment and diagnosis of cancerous tissue.Nevertheless,artificial-assisted EC diagnostics could also be employed with benefits in patients with inflammatory bowel disease(IBD)by accurately highlighting the presence of mucosal injury.In our review we included those studies comprising data about colonoscopy with narrow banding imaging and computer-aided diagnosis,as well as EC.Last but not least,artificial-assisted EC facilitates in-vivo diagnosis of the lower GI tract and may,in the future,remodel the field of in-vivo endoscopic diagnosis of colorectal lesions,representing another step towards the so-called optical biopsy.

Emerging role of deep learning-based artificial intelligence in tumor pathology

The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence(AI),especially deep learning(DL)-based AI,in tumor pathology.The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology,including tumor diagnosis,subtyping,grading,staging,and prognostic prediction,as well as the identification of pathological features,biomarkers and genetic changes.The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks.In addition,AI is useful for pathologists to meet the requirements of precision oncology.However,there are still some challenges relating to the implementation of AI,including the issues of algorithm validation and interpretability,computing systems,the unbelieving attitude of pathologists,clinicians and patients,as well as regulators and reimbursements.Herein,we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.