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Training Collections
Library Memberships
On-demand course library with video lectures, expert case reviews, and more
Fellowship Certificate™ Programs
Practice-focused training programs designed to help you gain experience in a specific subspecialty area.
Ultimate Learning Pass
Unlock access to our full Course Library and all self-paced Fellowships.
Continuing Medical Education (State CME)
Complete all of your state CME requirements in one convenient place.
Noon Conference (Free)
Get access to free live lectures, every week, from top radiologists.
Case of the Week (Free)
Get a free weekly case delivered right to your inbox.
Case Crunch: Rapid Case Review (Free)
Register for free live board reviews.
Dr. Resnick's MSK Conference
Learn directly from the MSK Master himself.
Lower Extremities MRI Conference
Musculoskeletal Imaging
Emergency Imaging
PET Imaging
Pediatric Imaging
For Training Programs
Supplement your training program with case-based learning for residents, registrars, fellows, and more.
For Private Practices
Upskill in high growth, advanced imaging areas.
Compliance
NewTrack, fulfill, and report on all your radiologists' credentialing and licensing requirements.
Emergency Call Prep
Prepare trainees to be on call for the emergency department with this specialized training series.
Lesson
Pre-Course Activities
1 topic, 1 min.
Lesson
Introduction to Tomosynthesis
7 topics, 21 min.
Welcome to Advanced Tomosynthesis Mastery Course
1 m.Introduction to Tomosynthesis (DBT) - Why Learn about Tomosynthesis?
3 m.Image Acquisition Technique, Positioning, and Physics
5 m.Screening & Diagnostic Performance of Tomosynthesis
4 m.CAD/AI in Tomosynthesis
2 m.Image Quality and Common Artifacts
8 m.Motion Artifact Case Example on Tomosynthesis
2 m.Lesson
Approach to Interpretation and Lesion Localization
5 topics, 23 min.
Lesson
Normal Imaging Findings
10 topics, 29 min.
Normal Findings - Palpable Lump Focal Fibroglandular Tissue
4 m.Normal Findings - Palpable Lump Unilateral Axillary
4 m.Normal Findings - Palpable Lump Bilateral Axillary
4 m.Normal Findings - Screening Mammogram - Calcifications
3 m.Normal Findings - Screening Mammogram - Dermal Calcifications
3 m.Normal Findings - Screening Mammogram - Deodorant Artifact Example 1
4 m.Normal Findings - Screening Mammogram - Deodorant Artifact Example 2
3 m.Normal Findings - Screening Mammogram - Deodorant Artifact Example 3
2 m.Normal Findings - History of Lumpectomy
4 m.Normal Findings - Diagnostic Mammogram after Right Breast Masses on Screening
4 m.Lesson
Asymmetries and Masses
8 topics, 24 min.
Mammographic Asymmetries and Masses - Overview
7 m.Asymmetry - Invasive Ductal Carcinoma
3 m.Focal Asymmetry - Focal Fibroglandular Tissue - Benign
4 m.Developing Asymmetry
4 m.Solitary Mass - Fibroadenoma
2 m.Solitary Mass - Malignancy
3 m.Multiple, Bilateral Masses - Case 1
3 m.Multiple, Bilateral Masses - Case 2
2 m.Lesson
Gynecomastia
3 topics, 4 min.
Lesson
Fat-containing Lesions
8 topics, 18 min.
Lesson
Architectural Distortion
5 topics, 17 min.
Architectural Distortion on Tomosynthesis - Imaging & Management - Overview
6 m.Pseudodistortion on Screening Mammogram, Normal on DBT
4 m.Architectural Distortion - Radial Scar / Complex Sclerosing Lesion
4 m.Architectural Distortion - Radiating Lines, Asymmetry / Invasive Ductal Carcinoma (IDC)
3 m.Architectural Distortion - Radiating Lines, Mass / Invasive Lobular Carcinoma (ILC)
3 m.Lesson
Calcifications
8 topics, 22 min.
Calcifications on Tomosynthesis - Overview
5 m.Pseudocalcifications - Case 1
3 m.Pseudocalcifications - Case 2
3 m.Calcifications - Typically Benign - Milk of Calcium - Case 1
3 m.Calcifications - Typically Benign - Milk of Calcium - Case 2
2 m.Calcifications - Suspicious - Amorphous / Malignancy (BI-RADS 4B)
4 m.Calcifications - Suspicious - Coarse Heterogeneous / Benign
2 m.Calcifications - Suspicious - Segmental Fine Linear Branching / Invasive Ductal Carcinoma (BI-RADS 4C)
4 m.Lesson
Associated Findings
7 topics, 18 min.
Associated Nipple Retraction, Palpable Mass / Invasive Ductal Carcinoma
3 m.Axillary Adenopathy - Unilateral / Chronic Lymphocytic Leukemia (CLL)
3 m.Axillary Adenopathy - Bilateral / History of Chronic Lymphocytic Leukemia (CLL)
2 m.Skin Thickening - Prior Lumpectomy, Post Radiation
2 m.Skin Thickening, Asymmetry - Inflammatory Carcinoma / Invasive Ductal Carcinoma
4 m.Skin Thickening, Calcifications - Inflammatory Carcinoma / Invasive Ductal Carcinoma
3 m.Skin Thickening, Mass - Locally Advanced Breast Carcinoma
3 m.Lesson
Postsurgical Findings
9 topics, 19 min.
Post Breast Reduction - Case 1
4 m.Post Breast Reduction - Case 2
2 m.Post Breast Reduction - Case 3
2 m.Post Lumpectomy - Benign Findings - Case 1
3 m.Post Lumpectomy - Benign Findings - Case 2
2 m.Post Lumpectomy - Recurrence
5 m.Post Lumpectomy - Benign Fat Necrosis
3 m.Silicone Injection
3 m.Concluding Remarks
1 m.0:00
So in terms of CAD and AI, um, CAD was initially
0:04
approved, uh, by the FDA in 1998 for use in
0:06
2D mammography. There's two different types of CAD,
0:10
uh, if you get into the details of it.
0:12
Uh, there's CAD-E, which is computer-aided detection,
0:15
which helps us to identify abnormalities, and there's
0:18
CAD-X, which is computer-aided diagnosis, meaning that
0:20
it classifies those abnormalities into, let's say...
0:25
CAD algorithms were originally
0:27
developed using supervised learning.
0:29
Um, were trained on datasets with known
0:31
pathology, and the radiologists who were
0:33
involved in those studies assessed positivity,
0:36
which overall led to some verification bias
0:38
and increased false positives over time.
0:41
Uh, I think the benefits of CAD have
0:44
been sort of whittled down, and so most
0:46
practices are probably not using CAD
0:51
as perhaps fully intended or doesn't
0:52
really help improve their performance.
0:55
But CAD based on AI, uh, does offer some
0:59
potential improvements over standard CAD.
1:02
Uh, in terms of training the algorithm, there's
1:05
unsupervised learning, meaning that AI is not
1:07
bound to radiologists for determination of truth.
1:10
Um, and that we can also have the potential
1:12
to do quantitative image analysis and to
1:14
see what the radiologist cannot see, right?
1:16
So it may look for all of us as radiologists for
1:19
all this, all the world looks exactly the same, but
1:21
the AI algorithm may pick up some area where there's
1:24
some underlying sort of abnormality or feature
1:29
that identifies that we can't really even perceive.
1:32
Um, so there's a lot of benefit for that.
1:34
Um, it's just being, starting to use it out
1:37
there in clinical practice or, um, being tested.
1:40
Um, and a lot of it remains kind of unknown
1:42
where, where CAD will end up with, uh, in.
CAD/AI in Tomosynthesis
HIDEInteractive Transcript
0:00
So in terms of CAD and AI, um, CAD was initially
0:04
approved, uh, by the FDA in 1998 for use in
0:06
2D mammography. There's two different types of CAD,
0:10
uh, if you get into the details of it.
0:12
Uh, there's CAD-E, which is computer-aided detection,
0:15
which helps us to identify abnormalities, and there's
0:18
CAD-X, which is computer-aided diagnosis, meaning that
0:20
it classifies those abnormalities into, let's say...
0:25
CAD algorithms were originally
0:27
developed using supervised learning.
0:29
Um, were trained on datasets with known
0:31
pathology, and the radiologists who were
0:33
involved in those studies assessed positivity,
0:36
which overall led to some verification bias
0:38
and increased false positives over time.
0:41
Uh, I think the benefits of CAD have
0:44
been sort of whittled down, and so most
0:46
practices are probably not using CAD
0:51
as perhaps fully intended or doesn't
0:52
really help improve their performance.
0:55
But CAD based on AI, uh, does offer some
0:59
potential improvements over standard CAD.
1:02
Uh, in terms of training the algorithm, there's
1:05
unsupervised learning, meaning that AI is not
1:07
bound to radiologists for determination of truth.
1:10
Um, and that we can also have the potential
1:12
to do quantitative image analysis and to
1:14
see what the radiologist cannot see, right?
1:16
So it may look for all of us as radiologists for
1:19
all this, all the world looks exactly the same, but
1:21
the AI algorithm may pick up some area where there's
1:24
some underlying sort of abnormality or feature
1:29
that identifies that we can't really even perceive.
1:32
Um, so there's a lot of benefit for that.
1:34
Um, it's just being, starting to use it out
1:37
there in clinical practice or, um, being tested.
1:40
Um, and a lot of it remains kind of unknown
1:42
where, where CAD will end up with, uh, in.
Report
Faculty
Ryan W. Woods, MD, MPH
Assistant Professor of Radiology
University of Wisconsin School of Medicine and Public Health
Tags
Women's Health
Tomosynthesis
Oncologic Imaging
Mammography
Breast
AI Technologies
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