Computational pathology, new horizons and challenges for anatomical pathology

dosSantos, Washington Luis Conrado; Freitas, Luiz Antônio Rodrigues de; Duarte, Ângelo; Ângelo, Michele Fúlvia; Oliveira, Luciano

doi:10.1186/s42047-022-00113-x

Cited by 4 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the past decade, AI has been used to model a wide spectrum of problems in histopathology, sometimes claiming super ‐ human performance [ 1 , 2 ]. Several recent review articles have covered CPath research trends from various perspectives [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Based on the level of analysis, application, and prediction variable(s), CPath algorithms can be broadly categorized into three groups: cell‐level, tissue‐level, and patient‐level (Figure 3 ).…”

Section: The Promise Of Ai In Computational Pathologymentioning

confidence: 99%

Unleashing the potential of AI for pathology: challenges and recommendations

Asif

Rajpoot

Graham

et al. 2023

The Journal of Pathology

View full text Add to dashboard Cite

Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer‐reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre‐clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

show abstract

Section: The Promise Of Ai In Computational Pathologymentioning

confidence: 99%

Unleashing the potential of AI for pathology: challenges and recommendations

Asif

Rajpoot

Graham

et al. 2023

The Journal of Pathology

View full text Add to dashboard Cite

show abstract

“…Although reversible slight podocyte lesions can usually be only perceived by electron microscopy analysis, severe podocyte degeneration can be diagnosed through the histological study of renal glomeruli on light microscopy. In recent years, several automated, machine learning-based methods have been proposed to aid in the visual analysis of renal tissue slides [6][7][8][9] . When writing this manuscript, the literature contained no references to computational models specifically focused on the automated classification of podocyte degenerative changes using images of renal glomeruli.…”

Section: Introductionmentioning

confidence: 99%

Enhancing podocyte degenerative changes recognition in glomerular images using convolutional neural networks

Barros

Silva

Proença

et al. 2023

Preprint

View full text Add to dashboard Cite

Podocyte degenerative changes are common in various kidney diseases, and their accurate identification is crucial for pathologists to diagnose and treat such conditions. However, this can be a difficult task, and previous attempts to automate the identification of podocytes have not been entirely successful. To address this issue, this study proposes a novel approach that combines pathologists’ expertise with an automated classifier to enhance the identification of podocytopathies. The study involved building a new dataset of renal glomeruli images, some with and others without podocyte degenerative changes, and developing an automated binary classifier based on Convolutional Neural Networks (CNN). The results showed that the automated classifier achieved an impressive 90.9% f-score. Moreover, when the pathologists used as an auxiliary tool to classify a second set of images, the medical group’s average performance increased significantly, from 91.4±12.5% to 96.1±2.9% of f-score. Fleiss’ kappa agreement among the pathologists also increased from 0.59 to 0.83. These findings suggest that artificial intelligence techniques based on convolutional neural networks can help pathologists correctly identify images of glomeruli with podocyte degeneration, leading to improved individual accuracy and greater agreement in diagnosing podocytopathies. This approach could have significant implications for the diagnosis and treatment of kidney diseases.

show abstract