Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network

Zhang, Ting; Chen, Juan; Lü, Yan; Yang, Xiu; Ouyang, Zhaolian

doi:10.1371/journal.pone.0273355

Cited by 9 publications

(2 citation statements)

References 95 publications

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“…DL-based methods have been shown to be efficient tools for dealing with ailment detection in the preprocessing, edge detection, extraction of features, categorization, and grouping processes. In this paper, the detection of disease in healthcare systems served as the focal point for evaluating the technical features of ML and DL architectures [92]. The effectiveness of these techniques was discussed in terms of algorithm parameters and the precision of disease identification.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Deep Learning Methods for Healthcare Sector - Medical Imaging Disease Detection

Sahu,

Kashyap,

Bhatia

et al. 2023

Contemp. Math.

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In this paper, artificial intelligence (AI) and the ideas of machine learning (ML) and deep learning (DL) are introduced gradually. Applying ML techniques like deep neural network (DNN) models has grown in popularity in recent years due to the complexity of healthcare data, which has been increasing. To extract hidden patterns and some other crucial information from the enormous amount of health data, which traditional analytics are unable to locate in a fair amount of time, ML approaches offer cost-effective and productive models for data analysis. We are encouraged to pursue this work because of the quick advancements made in DL approaches. The idea of DL is developing from its theoretical foundations to its applications. Modern ML models that are widely utilized in academia and industry, mostly in image classification and natural language processing, including DNN. Medical imaging technologies, medical healthcare data processing, medical disease diagnostics, and general healthcare all stand to greatly benefit from these developments. We have two goals: first, to conduct a survey on DL techniques for medical pictures, and second, to develop DL-based approaches for image classification. This paper is mainly targeted towards understanding the feasibility and different processes that could be adopted for medical image classification; for this, we perform a systematic literature review. A review of various existing techniques in terms of medical image classification indicates some shortcomings that have an impact on the performance of the whole model. This study aims to explore the existing DL approaches, challenges, brief comparisons, and applicability of different medical image processing are also studied and presented. The adoption of fewer datasets, poor use of temporal information, and reduced classification accuracy all contribute to the lower performance model, which is addressed. The study provides a clear explanation of contemporary developments, cutting-edge learning tools, and platforms for DL techniques.

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Section: Discussionmentioning

confidence: 99%

Analysis of Deep Learning Methods for Healthcare Sector - Medical Imaging Disease Detection

Sahu,

Kashyap,

Bhatia

et al. 2023

Contemp. Math.

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“…Moreover, it should be noted that there is inconsistency in coloration among different sections, even within a single hospital. This is attributed to various factors, including the xation degree in uenced by tissue size, variations in section thickness due to different procedures, and the selection of staining reagents during the H&E section preparation [ [23]]. These color and intensity variations in H&E-stained histological slides can impede the e cacy of quantitative image analysis [ [24]].…”

Section: Patch-level Image Ltering and Color Normalizationmentioning

confidence: 99%

A Pathology-Interpretable Deep Learning Model for Predicting Microsatellite Instability State in Colorectal Cancer: Validation across Diverse Platforms and Asian Cohorts

Zhang,

Wang,

Song

et al. 2024

Preprint

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Background The determination of microsatellite (MS) state plays a vital role in precise diagnosis and treatment of colorectal cancer (CRC). However, the limited availability of medical resources and challenging economic circumstances render MS state testing unattainable for a significant proportion of CRC patients. We propose a novel pathology-interpretable deep learning model to predict the MS state of CRC, with an inclination to validate in the Asian population across multiple cohorts and sequencing platforms. Methods Pathological images, documented MS state and clinical characteristics of 360 CRC patients from the cancer genome atlas together with 782 cases from Chinese hospital were included. Results The model demonstrated notable generalization ability, achieving an AUC of 0.92 in the independent verification cohort and an AUC of 0.93 in the multicenter cohort. We achieved cell nucleus segmentation and image-based cell type identification using the hover-net model to provide the pathology interpretation of the model, unveiling significant predictors of microsatellite instability. Notably, patients classified as microsatellite instability (MSI) by the model exhibited higher progression-free survival rates, supported by follow-up data. Conclusions The model shows great potential for clinical usage, particularly in the Asian population, demonstrating high accuracy across multiple cohorts and MSI detection platforms.

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Digital Pathology Ecosystem: Basic Elements to Revolutionize the Diagnosis and Monitoring of Diseases in Health Sector

Coccia

2024

Contributions to Management Science

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Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network

Cited by 9 publications

References 95 publications

Analysis of Deep Learning Methods for Healthcare Sector - Medical Imaging Disease Detection

Analysis of Deep Learning Methods for Healthcare Sector - Medical Imaging Disease Detection

A Pathology-Interpretable Deep Learning Model for Predicting Microsatellite Instability State in Colorectal Cancer: Validation across Diverse Platforms and Asian Cohorts

Digital Pathology Ecosystem: Basic Elements to Revolutionize the Diagnosis and Monitoring of Diseases in Health Sector

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