TÀI LIỆU HỘI NGHỊ KỸ THUẬT ĐIỆN QUANG VÀ Y HỌC HẠT NHÂN VIỆT NAM LẦN 7 (2019) Nguồn: https://hinhanhykhoa.com/

1. 8/13/2019
1
U d f K i Mi l ’ AI Cli i l D i i
The 21st Annual Vietnamese Congress of Radiology and Nuclear Medicine, 2019
Satoshi KasAI
Update of Konica Minolta’s AI Clinical Decision
Support Software
Satoshi KasAI
Clinical R&D Division, Image Processing Technology Department
X-ray Business Operations,
Healthcare Business Headquarters
History of AI
1960s 1970s 2010s1990s
～ ～ ～
Winter EraWinter Era
1980s 2000s
Limitations on teaching
expert knowledge
Limitations on solving
practical problems
Search and Inference
- The word “Artificial Intelligence” was
created
- Basic neural network was introduced
Knowledge-based AI with expert system
- Productization in a part of industrial area
Machine learning based AI
- Deep Learning
★”Automated diagnosis” was
discussed in a paper
★Concept of computer-aided diagnosis (CAD) was created
1995 SIANN
（U. of Chicago)
1998 R2 Technology 1st CAD product
KM CAD products
- CAD for mammogram（2010）
- Temporal Subtraction（2013）
- Bone Suppression（2015）
■Neural Network
Launched in 2010 to Japanese market
An example for a case with a very subtle
clustered microcalcifications

2. 8/13/2019
2
Target Diseases of Medical Image
Mammogram Chest X-ray
Breast cancer Lung Cancer, (Breast Cancer)
TBTB
Pneumonia
Cardiomegaly
Pneumothorax
PE
Atelectasis
Rib fractureRib fracture
Effusion
Emphysema
Fibrosis
Heart failure
Hernia
AI technology in 2nd Generation
age
re-
essing
ture
ction
sifica
on
sult
Input Output
✔Conventional method
Numerical conversion
A l O
Ima
Pr
proce
Feat
extra
Class
tio
Res
Ex.) region
extraction
Color red orange
Size large small
Shape circle ellipse
Texture
Sparse white
dot
Dense dots
RG
BArea
(pixel number)
Circularity
SD
Apple Orange
(158,46,36) (242,118,26)
100cm2
(2000)
25cm2
(500)
0.90 0.86
11.1 20.2
Original Image Bone
Suppression
Bone Suppression
© 2016 Konica Minolta, Inc.
Bone Suppression
hinhanhykhoa.com

3. 8/13/2019
3
History of AI
1960s 1970s 2010s1990s
～ ～ ～
Winter EraWinter Era
1980s 2000s
Limitations on teaching
expert knowledge
Limitations on solving
practical problems
Search and Inference
- The word “Artificial Intelligence” was
created
- Basic neural network was introduced
Knowledge-based AI with expert system
- Productization in a part of industrial area
Machine learning based AI
- Deep Learning
★”automated diagnosis” was
discussed in a paper
★concept of computer-aided diagnosis (CAD) was created
1995 SIANN
（U. of Chicago)
1998 R2 Technology 1st CAD product
KM CAD products
- CAD for mammogram（2010）
- Temporal Subtraction（2013）
- Bone Suppression（2015）
■Neural Network
Conventional vs Deep Learning
age
re-
essing
ture
ction
sifica
on
sult
Input Output
✔Conventional method
Ima
Pr
proce
Feat
extra
Class
tio
Res
e
sing
re
ion
fica
ult
Input Output
✔Deep Learning
Imag
Pre-
process
Featur
extracti
Classif
tion
Resu

4. 8/13/2019
4
Target Diseases of Medical Image
Mammogram Chest X-ray
Breast cancer Lung Cancer, (Breast Cancer)
TBTB
Pneumonia
Cardiomegaly
Pneumothorax
PE
Atelectasis
Rib fractureRib fracture
Effusion
Emphysema
Fibrosis
Heart failure
Hernia
■Will show Chest AI Demo
① Several usage of chest AI : Triage #1
- AI will prioritize cases.
Cases with high priority will be
Notification
AI
Radiologist
- Cases with high priority will be
delivered and noticed to doctors right
after image creation.
✔Value hypothesis
- Reducing time for physician to access abnormal cases
- Next action could be determined within a same day.
- The image will be
interpreted by a radiologist.
- Simultaneously, AI checks
② Several usage of chest AI : Q/A
Diagno
sis
Same
the image.
- In case the results of
doctors and AI are not
matched, those cases will
be sent to 2nd doctors (or
same doctor).
Radiologist
AI
Radiologist
or NLP
2nd or same
Radiologist
sis
Discrepancy
Check
16
✔Value hypothesis
- Improve accuracy of CXR interpretations without too much labor
with dual interpretation

5. 8/13/2019
5
- AI will be used as 1st reader.
- In case images are
classified as normal by
③ Several usage of chest AI : 1st Reader
Diagnosis
Normal
classified as normal by
computer, radiologists may
not interpret the image (or
check briefly).
✔Value hypothesis
AI
radiologist
Abnormal
✔Value hypothesis
- Doctors could focus on potential abnormal cases. It’s significant reduce
of doctors’ labor.
※Regulatory must be clarified for this usage. It’s same as other usages, but must be verified carefully.
■To improve the environment of chest x-ray diagnosis is
Take home message #1
y g
one of Konica Minolta’s high priority projects on AI
Focus on Primary Care
■ Line-up of Konica Minolta Products for primary care
■ Create movie-like images with use of pulse x-ray.
■ X-ray condition (ex.)
About Dynamic X-ray
Pulse X-ray
・ 95 kV
・ 15 flame/sec.
・ 10～20 sec.
・ pixel：0.4mm×0.4mm
■ Dose
・ 7 3 uGy/flame
Upright
Position
y
Device
Continuous
pulse x-ray
・ 7.3 uGy/flame
In case 15 sec.,
7.3 uGy×15 flame×15 sec
=1.66mGy
・ Below IAEA guidance level
（PA＋lateral）1.9mGy

6. 8/13/2019
6
Dynamic imaging x bone suppression
Original Bone Suppression
From Anatomical to Kinetic
COPD
From Kinetic to Functional Blood Depth Changes
+10mm
Normal Abnormal
‐10mm

7. 8/13/2019
7
Maxima Phase Shift Functional Images
+0.50.5
Normal Abnormal
‐‐0.5
■ Primary care is one of main targets for Konica Minolta
– Konica Minolta has products
Take home message #2
Konica Minolta has products
• Flat Panel Detector (including dynamic imaging)
• Ultrasound
• PACS/WS
• AI (currently in Japan only)
– Computer-aided Detection for Mammogram
– Bone Suppression Images for Chest X-ray
– Others
Confidential