Artificial Intelligence in Digital Pathology: What Is the Future? Part 2: An Investigation on the Insiders

Motivation: This study deals with the introduction of artificial intelligence (AI) in digital pathology (DP). The study starts from the highlights of a companion paper. Objective: The aim was to investigate the consensus and acceptance of the insiders on this issue. Procedure: An electronic survey based on the standardized package Microsoft Forms (Microsoft, Redmond, WA, USA) was proposed to a sample of biomedical laboratory technicians (149 admitted in the study, 76 males, 73 females, mean age 44.2 years). Results: The survey showed no criticality. It highlighted (a) the good perception of the basic training on both groups, and (b) a uniformly low perceived knowledge of AI (as arisen from the graded questions). Expectations, perceived general impact, perceived changes in the work-flow, and worries clearly emerged in the study. Conclusions: The of AI in DP is an unstoppable process, as well as the increase of the digitalization in the health domain. Stakeholders must not look with suspicion towards AI, which can represent an important resource, but should invest in monitoring and consensus training initiatives based also on electronic surveys.

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Lessons from the COVID-19 Pandemic on the Use of Artificial Intelligence in Digital Radiology: The Submission of a Survey to Investigate the Opinion of Insiders

Healthcare ◽

10.3390/healthcare9030331 ◽

2021 ◽

Vol 9 (3) ◽

pp. 331

Author(s):

Daniele Giansanti ◽

Ivano Rossi ◽

Lisa Monoscalco

Keyword(s):

Artificial Intelligence ◽

General Purpose ◽

Point Of View ◽

General Point ◽

Thoracic Radiography ◽

Digital Radiology ◽

Electronic Survey ◽

Radiology Technicians ◽

Medical Radiology ◽

Great Development

The development of artificial intelligence (AI) during the COVID-19 pandemic is there for all to see, and has undoubtedly mainly concerned the activities of digital radiology. Nevertheless, the strong perception in the research and clinical application environment is that AI in radiology is like a hammer in search of a nail. Notable developments and opportunities do not seem to be combined, now, in the time of the COVID-19 pandemic, with a stable, effective, and concrete use in clinical routine; the use of AI often seems limited to use in research applications. This study considers the future perceived integration of AI with digital radiology after the COVID-19 pandemic and proposes a methodology that, by means of a wide interaction of the involved actors, allows a positioning exercise for acceptance evaluation using a general purpose electronic survey. The methodology was tested on a first category of professionals, the medical radiology technicians (MRT), and allowed to (i) collect their impressions on the issue in a structured way, and (ii) collect their suggestions and their comments in order to create a specific tool for this professional figure to be used in scientific societies. This study is useful for the stakeholders in the field, and yielded several noteworthy observations, among them (iii) the perception of great development in thoracic radiography and CT, but a loss of opportunity in integration with non-radiological technologies; (iv) the belief that it is appropriate to invest in training and infrastructure dedicated to AI; and (v) the widespread idea that AI can become a strong complementary tool to human activity. From a general point of view, the study is a clear invitation to face the last yard of AI in digital radiology, a last yard that depends a lot on the opinion and the ability to accept these technologies by the operators of digital radiology.

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

Healthcare ◽

10.3390/healthcare9010030 ◽

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 [...]

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An Integrated Digital Pathology Platform for Tumors Using Artificial Intelligence Analysis

SSRN Electronic Journal ◽

10.2139/ssrn.3935673 ◽

2021 ◽

Author(s):

Changjiang Zhou ◽

Xiaobing Feng ◽

Hongbin Cai ◽

Yi Jin ◽

Harvest F. Gu ◽

...

Keyword(s):

Artificial Intelligence ◽

Digital Pathology ◽

Intelligence Analysis

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Modeling Belief in Dynamic Systems, Part II: Revision and Update

Journal of Artificial Intelligence Research ◽

10.1613/jair.506 ◽

1999 ◽

Vol 10 ◽

pp. 117-167 ◽

Cited By ~ 20

Author(s):

N. Friedman ◽

J. Y. Halpern

Keyword(s):

Artificial Intelligence ◽

Belief Revision ◽

Dynamic Systems ◽

Belief Change ◽

Companion Paper ◽

Minimal Change ◽

Special Cases ◽

Epistemic Modalities ◽

Over Time ◽

New Framework

The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a companion paper (Friedman & Halpern, 1997), we introduce a new framework to model belief change. This framework combines temporal and epistemic modalities with a notion of plausibility, allowing us to examine the change of beliefs over time. In this paper, we show how belief revision and belief update can be captured in our framework. This allows us to compare the assumptions made by each method, and to better understand the principles underlying them. In particular, it shows that Katsuno and Mendelzon's notion of belief update (Katsuno & Mendelzon, 1991a) depends on several strong assumptions that may limit its applicability in artificial intelligence. Finally, our analysis allow us to identify a notion of minimal change that underlies a broad range of belief change operations including revision and update.

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Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer

Journal of Clinical Pathology ◽

10.1136/jclinpath-2020-207351 ◽

2021 ◽

pp. jclinpath-2020-207351

Author(s):

Jenny Fitzgerald ◽

Debra Higgins ◽

Claudia Mazo Vargas ◽

William Watson ◽

Catherine Mooney ◽

...

Keyword(s):

Prostate Cancer ◽

Artificial Intelligence ◽

Patient Outcome ◽

Image Analysis ◽

High Throughput Screening ◽

Personalised Medicine ◽

Digital Pathology ◽

Disease Stage ◽

Breast And Prostate Cancer ◽

Biomarker Evaluation

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.

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User Feedback to Improve the Performance of a Cyberattack Detection Artificial Intelligence System in the e-Health Domain

10.1007/978-3-030-85607-6_25 ◽

2021 ◽

pp. 295-299

Author(s):

Carmelo Ardito ◽

Tommaso Di Noia ◽

Eugenio Di Sciascio ◽

Domenico Lofù ◽

Andrea Pazienza ◽

...

Keyword(s):

Artificial Intelligence ◽

User Feedback ◽

Artificial Intelligence System ◽

Health Domain ◽

Intelligence System

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Multiresolution Application of Artificial Intelligence in Digital Pathology for Prediction of Positive Lymph Nodes From Primary Tumors in Bladder Cancer

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00155 ◽

2020 ◽

pp. 367-382 ◽

Cited By ~ 2

Author(s):

Stephanie A. Harmon ◽

Thomas H. Sanford ◽

G. Thomas Brown ◽

Chris Yang ◽

Sherif Mehralivand ◽

...

Keyword(s):

Artificial Intelligence ◽

Digital Pathology ◽

Area Under The Curve ◽

The Cancer Genome Atlas ◽

Clinicopathologic Features ◽

Combined Model ◽

Primary Tumors ◽

Logistic Regression Models ◽

Spatially Resolved ◽

Digital Evaluation

PURPOSE To develop an artificial intelligence (AI)–based model for identifying patients with lymph node (LN) metastasis based on digital evaluation of primary tumors and train the model using cystectomy specimens available from The Cancer Genome Atlas (TCGA) Project; patients from our institution were included for validation of the leave-out test cohort. METHODS In all, 307 patients were identified for inclusion in the study (TCGA, n = 294; in-house, n = 13). Deep learning models were trained from image patches at 2.5×, 5×, 10×, and 20× magnifications, and spatially resolved prediction maps were combined with microenvironment (lymphocyte infiltration) features to derive a final patient-level AI score (probability of LN metastasis). Training and validation included 219 patients (training, n = 146; validation, n = 73); 89 patients (TCGA, n = 75; in-house, n = 13) were reserved as an independent testing set. Multivariable logistic regression models for predicting LN status based on clinicopathologic features alone and a combined model with AI score were fit to training and validation sets. RESULTS Several patients were determined to have positive LN metastasis in TCGA (n = 105; 35.7%) and in-house (n = 3; 23.1%) cohorts. A clinicopathologic model that considered using factors such as age, T stage, and lymphovascular invasion demonstrated an area under the curve (AUC) of 0.755 (95% CI, 0.680 to 0.831) in the training and validation cohorts compared with the cross validation of the AI score (likelihood of positive LNs), which achieved an AUC of 0.866 (95% CI, 0.812 to 0.920; P = .021). Performance in the test cohort was similar, with a clinicopathologic model AUC of 0.678 (95% CI, 0.554 to 0.802) and an AI score of 0.784 (95% CI, 0.702 to 0.896; P = .21). In addition, the AI score remained significant after adjusting for clinicopathologic variables ( P = 1.08 × 10−9), and the combined model significantly outperformed clinicopathologic features alone in the test cohort with an AUC of 0.807 (95% CI, 0.702 to 0.912; P = .047). CONCLUSION Patients who are at higher risk of having positive LNs during cystectomy can be identified on primary tumor samples using novel AI-based methodologies applied to digital hematoxylin and eosin–stained slides.

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Rocky road to digital diagnostics: implementation issues and exhilarating experiences

Journal of Clinical Pathology ◽

10.1136/jclinpath-2020-206715 ◽

2020 ◽

pp. jclinpath-2020-206715

Author(s):

Nikolaos Stathonikos ◽

Tri Q Nguyen ◽

Paul J van Diest

Keyword(s):

Artificial Intelligence ◽

Patient Safety ◽

Student Teaching ◽

Digital Pathology ◽

Digital Archive ◽

Teaching And Research ◽

Multidisciplinary Meetings ◽

Slide Scanner

Since 2007, we have gradually been building up infrastructure for digital pathology, starting with a whole slide scanner park to build up a digital archive to streamline doing multidisciplinary meetings, student teaching and research, culminating in a full digital diagnostic workflow where we are currently integrating artificial intelligence algorithms. In this paper, we highlight the different steps in this process towards digital diagnostics, which was at times a rocky road with definitely issues in implementation, but eventually an exciting new way to practice pathology in a more modern and efficient way where patient safety has clearly gone up.

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