III — Diagnostic Imaging, Modality by Modality
14
Ultrasound & Echocardiography
AI in Medical Imaging
Preface
I — The Landscape of Medical Imaging
1
Introduction: Why AI in Medical Imaging
2
The Universe of Medical Images
3
How Images Live in a Hospital
4
Medical Image Viewers
II — Foundations: Machine Learning & Computer Vision
5
Machine Learning: The Concepts
6
What Computer Vision Can Do
7
Deep Learning Architectures
8
Generative & Frontier Models
9
Agentic AI in Medical Imaging
10
From Model to Product: Evaluation, Regulation, Deployment
III — Diagnostic Imaging, Modality by Modality
11
Chest X-ray
12
Computed Tomography (CT)
13
Magnetic Resonance Imaging (MRI)
14
Ultrasound & Echocardiography
15
Mammography & Breast Imaging
16
Nuclear Medicine: PET & SPECT
17
Ophthalmic Imaging: Fundus & OCT
IV — Interventional, Lab-Based & Video
18
Surgical & Endoscopic Video
19
Digital Pathology & Histology
20
The Remaining Map
V — Systems & The Road Ahead
21
Building an Imaging AI Platform
22
Ethics, Bias, Safety & The Future
Appendices
The Dataset Directory
The Model Zoo
FDA-Cleared AI Product Index
DICOM Quick Reference for ML Engineers
Dual Glossary
Table of contents
14.1
What is ultrasound?
14.2
Why and when it’s ordered
14.3
What diagnoses are made from it
14.4
How it works in a modern hospital
14.5
The data landscape
14.6
The model landscape
14.7
FDA-cleared AI products
14.8
Open challenges
14.9
The agentic outlook
14.10
Further reading
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III — Diagnostic Imaging, Modality by Modality
14
Ultrasound & Echocardiography
14
Ultrasound & Echocardiography
Note
Draft chapter — outline only. Content coming soon.
14.1
What is ultrasound?
14.2
Why and when it’s ordered
14.3
What diagnoses are made from it
14.4
How it works in a modern hospital
14.5
The data landscape
14.6
The model landscape
14.7
FDA-cleared AI products
14.8
Open challenges
14.9
The agentic outlook
14.10
Further reading
13
Magnetic Resonance Imaging (MRI)
15
Mammography & Breast Imaging