scholarly journals Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice

2019 ◽  
Vol 249 (2) ◽  
pp. 143-150 ◽  
Author(s):  
Richard Colling ◽  
Helen Pitman ◽  
Karin Oien ◽  
Nasir Rajpoot ◽  
Philip Macklin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Digital Pathology

scholarly journals Deep Learning Based Retrieval System for Gigapixel Histopathology Cases and Open Access Literature

2018 ◽  
Author(s):  
Sebastian Otálora ◽  
Roger Schaer ◽  
Oscar Jimenez-del-Toro ◽  
Manfredo Atzori ◽  
Henning Müller
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Retrieval System ◽  
Digital Pathology ◽  
Image Features ◽  
Retrieval Performance ◽  
Slide Image ◽  
Text Information ◽  
Computation Algorithms ◽  
Whole Slide Images

ABSTRACTClinical practice is getting increasingly stressful for pathologists due to increasing complexity and time constraints. Histopathology is slowly shifting to digital pathology, thus creating opportunities to allow pathologists to improve reading quality or save time using Artificial Intelligence (AI)-based applications. We aim to enhance the practice of pathologists through a retrieval system that allows them to simplify their workflow, limit the need for second opinions, while also learning in the process. In this work, an innovative retrieval system for digital pathology is integrated within a Whole Slide Image (WSI) viewer, allowing to define regions of interest in images as queries for finding visually similar areas using deep representations. The back-end similarity computation algorithms are based on a multimodal approach, allowing to exploit both text information and content-based image features. Shallow and deep representations of the images were evaluated, the later showed a better overall retrieval performance in a set of 112 whole slide images from biopsies. The system was also tested by pathologists, highlighting its capabilities and suggesting possible ways to improve it and make it more usable in clinical practice. The retrieval system developed can enhance the practice of pathologists by enabling them to use their experience and knowledge to properly control artificial intelligence tools for navigating repositories of images for decision support purposes.

scholarly journals Artificial Intelligence (AI) in Pathology – A Summary and Challenges

2021 ◽  
pp. 23-34
Author(s):  
Archana Buch ◽  
Rohan Kulkarni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Practice ◽  
Data Quality ◽  
Digital Pathology ◽  
Regulatory Approval ◽  
Cost Data ◽  
Unsupervised Machine Learning ◽  
Implementation Challenges ◽  
Bibliographic Study

This bibliographic study covers Artificial Intelligence (AI)theory and its applications from the healthcare field and in particular from the discipline of pathology. This review includes basics of AI, supervised and unsupervised machine learning (ML), various supervised ML algorithms, and their applications in healthcare and pathology. Digital Pathology with Deep Machine Learning is more advantageous over traditional pathology that is based on ‘physical slide on a physical microscope’. However, various implementation challenges of cost, data quality, multi-center validation, bias, and regulatory approval issues for AI in clinical practice still remain, which are also described in this study.

scholarly journals Interventional radiology and artificial intelligence in radiology: Is it time to enhance the vision of our medical students?

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Pierre Auloge ◽  
Julien Garnon ◽  
Joey Marie Robinson ◽  
Sarah Dbouk ◽  
Jean Sibilia ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Interventional Radiology ◽  
Clinical Practice ◽  
Medical Students ◽  
Medical Curriculum ◽  
New Technology ◽  
Large Population ◽  
The Other ◽  
Clinical Phase ◽  
Preclinical Phase

Abstract Objectives To assess awareness and knowledge of Interventional Radiology (IR) in a large population of medical students in 2019. Methods An anonymous survey was distributed electronically to 9546 medical students from first to sixth year at three European medical schools. The survey contained 14 questions, including two general questions on diagnostic radiology (DR) and artificial intelligence (AI), and 11 on IR. Responses were analyzed for all students and compared between preclinical (PCs) (first to third year) and clinical phase (Cs) (fourth to sixth year) of medical school. Of 9546 students, 1459 students (15.3%) answered the survey. Results On DR questions, 34.8% answered that AI is a threat for radiologists (PCs: 246/725 (33.9%); Cs: 248/734 (36%)) and 91.1% thought that radiology has a future (PCs: 668/725 (92.1%); Cs: 657/734 (89.5%)). On IR questions, 80.8% (1179/1459) students had already heard of IR; 75.7% (1104/1459) stated that their knowledge of IR wasn’t as good as the other specialties and 80% would like more lectures on IR. Finally, 24.2% (353/1459) indicated an interest in a career in IR with a majority of women in preclinical phase, but this trend reverses in clinical phase. Conclusions Development of new technology supporting advances in artificial intelligence will likely continue to change the landscape of radiology; however, medical students remain confident in the need for specialty-trained human physicians in the future of radiology as a clinical practice. A large majority of medical students would like more information about IR in their medical curriculum; almost a quarter of students would be interested in a career in IR.

scholarly journals How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xinran Wang ◽  
Liang Wang ◽  
Hong Bu ◽  
Ningning Zhang ◽  
Meng Yue ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Clinical Practice ◽  
Subjective Assessment ◽  
Current Guideline ◽  
Scoring Method ◽  
Programmed Death ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Scoring Accuracy

AbstractProgrammed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936–0.962) from moderate in RS1 (0.674, 95% CI: 0.614–0.735) and RS2 (0.736, 95% CI: 0.683–0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953–0.964) and 13% (0.815, 95% CI: 0.803–0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% “fully accepted” and 91% “almost accepted”. The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice.

scholarly journals The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

Healthcare ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 30
Author(s):  
Daniele Giansanti
Keyword(s):  
Artificial Intelligence ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Mobile Health ◽  
Digital Pathology ◽  
Digital Radiology ◽  
Cellular Diagnostics ◽  
Information And Communication ◽  
Electronic Health ◽  
New Applications

Thanks to the incredible changes promoted by Information and Communication Technology (ICT) conveyed today by electronic-health (eHealth) and mobile-health (mHealth), many new applications of both organ and cellular diagnostics are now possible [...]

An Integrated Digital Pathology Platform for Tumors Using Artificial Intelligence Analysis

2021 ◽  
Author(s):  
Changjiang Zhou ◽  
Xiaobing Feng ◽  
Hongbin Cai ◽  
Yi Jin ◽  
Harvest F. Gu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Digital Pathology ◽  
Intelligence Analysis

Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations

2020 ◽  
Vol 30 (6) ◽  
pp. 3576-3584 ◽  
Author(s):  
Michael P. Recht ◽  
Marc Dewey ◽  
Keith Dreyer ◽  
Curtis Langlotz ◽  
Wiro Niessen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice

scholarly journals Physicians’ requirements and expectations of future Artificial Intelligence applications for healthcare – a web-based survey in German university hospitals (Preprint)

2020 ◽  
Author(s):  
Oliver Maassen ◽  
Sebastian Fritsch ◽  
Julia Gantner ◽  
Saskia Deffge ◽  
Julian Kunze ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Patient Care ◽  
Biomedical Research ◽  
Positive Attitude ◽  
Patient Data ◽  
University Hospitals ◽  
Web Based ◽  
Wide Range ◽  
Medical Disciplines

BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of healthcare. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research has not been investigated widely in German university hospitals. OBJECTIVE Evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research e.g. for the development of machine learning (ML) algorithms in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given on Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS 121 (39.9%) female and 173 (57.1%) male physicians (N=303) from a wide range of medical disciplines and work experience levels completed the online survey. The majority of respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). 82.5% of respondents (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism, there come several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (e.g., imaging procedures in radiology and pathology) or data is collected continuously (e.g. cardiology and intensive care medicine), physicians’ expectations to substantially improve future patient care are high. However, for the practical usage of AI in healthcare regulatory and organizational challenges still have to be mastered.

scholarly journals A neural network for glomerulus classification based on histological images of kidney biopsy

2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Giacomo Donato Cascarano ◽  
Francesco Saverio Debitonto ◽  
Ruggero Lemma ◽  
Antonio Brunetti ◽  
Domenico Buongiorno ◽  
...  
Keyword(s):  
Neural Network ◽  
Clinical Practice ◽  
Periodic Acid ◽  
Digital Pathology ◽  
Feature Reduction ◽  
Training Procedure ◽  
Good Precision ◽  
Periodic Acid Schiff ◽  
Cad Systems ◽  
Cad System

Abstract Background Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results. CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists. Results We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes. We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation < 0.00), respectively. We also obtained good precision (0.9844 ± 0.0111) and recall (0.9310 ± 0.0153). Conclusions Results on the test set confirm that the proposed workflow is consistent and reliable for the investigated domain, and it can support the clinical practice of discriminating the two classes of glomeruli. Analyses on misclassifications show that the involved images are usually affected by staining artefacts or present partial sections due to slice preparation and staining processes. In clinical practice, however, pathologists discard images showing such artefacts.

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