Digital Pathology, 1st Edition - Implementation in Clinical Practice with AI applications (24년 6월 출간예정)

Authors: Meera Hameed, Matthew G Hanna

ISBN: 9780443138096

Imprint: Academic Press

Published: June, 2024

Paperback

Description

Digital Pathology: Implementation in Clinical Practice with AI Applications covers digital pathology applications in a clinical pathology laboratory setting, providing guidance and various tools for practical implementation. This comprehensive and detailed description of nuts and bolts of digital pathology implementation is unique and comes from the experience of high throughput scanning from a large surgical pathology laboratory. This is a valuable reference to prepare the end users not only about thoughtful launch of digital pathology but also to prepare for the future use of AI mediated decision support tools as the field advances.This book covers the entire range of this topic and can be used as a practical reference for individuals that wish to be part of the evolution of diagnostic pathology. Sections include pre-analytic, analytic, and post-analytic processes, encompassing hardware, software, space, staffing, quality control, laboratory information system applications, clinical validation, use cases and future applications, including computer assisted diagnosis and decision support tools.

Key features

• Provides detailed aspects of the implementation of digital pathology in the clinical laboratory, including machine learning/artificial intelligence tools

• Presents examples of how digital imaging and AI are drivers in research and development in radiology, with parallels to situations in pathology

• Includes best practices and guidelines that can be practically applied by pathologists and data scientists

Readership

Pathologists, Machine Learning Scientists, Trainees Industry professionals related to Digital and Computational Pathology

Table of contents

1. Basics of Digital Pathology

2. The 5 S’s for Success (Sponsorship, Staffing, Storage, Space, Scanners)

3. Pre-analytics

4. Analytic and post-analytic factors

5. Use cases in Anatomic Pathology

6. Use Cases in Clinical Pathology

7. The Pathologist’s Portal: optimal integration of digital data

8. Regulatory aspects of digital pathology

9. The basics of Machine Learning and AI

10. Machine Learning and AI applications

11. Integration of Diagnostic Imaging in Radiology and Pathology

About the authors

Meera Hameed

Dr. Hameed is the Chief of Surgical Pathology overseeing a large surgical pathology laboratory with over 80,000 cases and generating over a million slides per year. In the last several years her division has undertaken the task of digitizing slides (retrospective and prospective) using multiple platforms with a vision is to incorporate the use of digital information and digital information systems into all aspects of Pathology. She is also the Co-Director for the Warren Alpert Center for Digital and Computational Pathology at her institution. Through the Center an infrastructure has been established for deep learning which includes a high-performance computing cluster with a solid foundation to foster collaborations among experimental and diagnostic pathologists, computer and imaging scientists and experts in deep learning and artificial intelligence.

Matthew G Hanna

Dr. Hanna is the Director of Digital Pathology Informatics at Memorial Sloan Kettering Cancer Center. He leads the digital transformation of the pathology department as it related to clinical implementation and translation of digital pathology. Dr Hanna speaks regularly at international and domestic conferences on the successful deployment of digital pathology at his institution. The large pathology department has demonstrated a high volume of prospective clinical scanning, and retrospective archival digitization of over 5 million slides to date. Dr Hanna has also collaborated with computational pathology researchers to develop and integrate clinical decision support tools in the Department. Through the early adopter status and developed infrastructure at the institution, practical implementation steps and lessons learned can be used as a blueprint for other institutions to enable success