인공지능과 디지털 치의학 2021.06 / 이마고웍스(주) 김영준

1. AI & Digital Dentistry
2021. 6.
이마고웍스 (주)
김 영 준

2. https://www.youtube.com/watch?v=NxQSxM0OkkY

3. https://www.WhichFaceIsReal.com/
Which Face is Real?

4. AlphaGo Zero
4

5. Alpha Zero
"Mastering Chess and Shogi by Self-Play
with a General Reinforcement Learning Algorithm"

8. Hype Cycle for Artificial Intelligence, 2020

9. 출처: NVIDIA

10. 출처: NVIDIA
매일 350백만 개의 이미지 업로드
빅 데이터
시간당 2.5 Petabytes 고객데이터 생성
매분당 100시간의 비디오들이 업로드
새로운 딥러닝 기술 GPU 가속

11. https://eunsour.tistory.com/48

12. ▪ Logistic regression: No hidden layer
▪ Neural Networks: 1 hidden layer
▪ Deep NN: N hidden layer (N>1)

13. https://eunsour.tistory.com/48

14. https://thebook.io/080244/part04/unit16/06/

15. Datasets: Train, Validation, and Test

16. https://www.quora.com/How-do-I-learn-Neural-Network-by-myself

17. G. Litjens, et al.
A Survey on Deep Learning
in Medical Image Analysis,
Med Img Anal, 2021

18. https://ataspinar.com/wp-content/uploads/2017/11/deeplearing_types.png
Classification
Object Detection
Segmentation
bunny
1 : Duck, [x1, y1, x2, y2]
2 : Duck, [x1, y1, x2, y2]
3 : Bunny, [x1, y1, x2, y2]

19. Class Activation Map (CAM)
– Visual Explanation of Deep Learning
Explainable AI

20. Deep Learning & Digital Dentistry

21. Diagnosis assistance &
Recommendation of treatment plan
Automated processing
of patient data
(X-ray, CBCT, 3D scan, …)
Automated design
of prosthetic appliance
Deep Learning Applications in Dentistry
AI AI

22. Chen, Y.W. et al., Quintessence Int., 51(3), 2020
Deep Learning Applications in Dentistry

23. Deep Learning Applications in Dentistry
Chen, Y.W. et al., Quintessence Int., 51(3), 2020

24. Deep Learning Applications in Dentistry
An AI-based technology used in the detection
of possible pathologic lesions.
Suspected caries lesions in the bitewing
radiograph are identified in the software.
Image courtesy of VideaHealth
Chen, Y.W. et al., Quintessence Int., 51(3), 2020

25. Click to add text

26. Evaluation of Transfer Learning
with Deep Convolutional Neural Networks
for Screening Osteoporosis
in Dental Panoramic Radiographs

27. Deep Convolutional Neural Networks Based Analysis of
Cephalometric Radio Radiographs for Differential Diagnosis
of Orthognathic Surgery Indications

28. Transfer Learning via Deep Convolutional Neural Networks for
Implant Fixture System Classification Using Periapical Radiographs

29. DeNTNet:
Deep Neural Transfer Network for the detection of periodontal bone loss
using panoramic dental radiographs

30. Promaton Apox
Promaton® Apox: Panoramic x-ray, Periapical or
Bitewing x-ray can be turned into clinical insights.
https://www.promaton.com/

31. Cephalometric Analysis on X-ray Scans
▪ Auto. Landmark Detection
▪ Auto. Analysis
▪ Auto. Head Pose Correction
▪ Additional func.
▪ Superimposition
▪ Profile overlay
https://orca-ai.com/

32. Cephalometric Analysis (2D) on CBCT
▪ Convert CBCT to 2D ceph. style image
▪ Auto. Landmark Detection
▪ Auto. Analysis (2D)
▪ Additional func.
▪ Superimposition
▪ Profile overlay
▪ Reports
https://orca-ai.com/

33. Web-based fully automated
cephalometric analysis by deep learning

34. CEPPRO LaonCeph
V-Ceph
WebCeph

35. https://hellopearl.com/

36. Managing Patient List
https://orca-ai.com/

37. https://hellopearl.com/

38. Promaton 3D
https://www.promaton.com/

39. CBCT Segmentation
▪ Auto. Segmentation
▪ Teeth
▪ Bone
▪ Nerve Canal (Location)
▪ Airway
▪ STL export
https://orca-ai.com/

40. Labeling Tool

41. Patient
CT Slices
Input
CT Slices
Proposed
CNN
Output
2D Segmentation
Reconstructed
3D Segmentation
Fully-automated Segmentation of Anatomic Models

42. Fully-automated Segmentation of Anatomic Models
Deep learning algorithm
for CT segmentation
Automated segmentation of
mandible/maxilla

43. Real-time deep learning-based segmentation in web browser

44. Dice Coefficient : 97.6
Surface Distance : 0.033 mm
Process time: ~ 15s
딥러닝 기반 치과용 3D CT 처리 자동화

45. Deep learning algorithm for
CT segmentation
딥러닝 기반 CT 상하악골 모델링 결과
딥러닝 기반 치과용 3D CT 처리 자동화
상하악골/개별치아/신경관 분할 및 모델링 자동화, 3D 특징점 자동 인식

46. • 3D anatomical landmark detection using CBCT
• Registration of CBCT and 3D dental scan model
High inter- and intra-observer variability
Lots of time and efforts
딥러닝 기반 치과용 CT 데이터 처리 자동화
상하악골/개별치아/신경관 분할 및 모델링 자동화, 3D 특징점 자동 인식

47. 딥러닝 기반 치과용 CT 데이터 처리 자동화
상하악골/개별치아/신경관 분할 및 모델링 자동화, 3D 특징점 자동 인식
CBCT Volume
Create Projections
(Coronal / Sagittal)
Deep Neural Network
…
Initial Detection
…
VOI Centered with
Each Initial Landmarks
…
3D Deep Neural Network Final Detection
First
Stage
Second
Stage

48. Multi-Center Datasets

49. 49/49
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
Point-to-point
Error
(mm)
Transfer Learning & Results

50. 50/49
Transfer Learning & Results

51. 딥러닝 기반 3D 구강 스캔 데이터 처리 자동화
▪ 비정형 데이터 딥러닝을 통한 3D 구강 스캔 데이터의 개별 치아 자동 분할
AI

52. 3D 구강 스캔 & CT 자동 정합 (fully-automated)
딥러닝 기반 3D 구강 스캔, CT 데이터 정합 자동화

53. 딥러닝 기반 3D 구강 스캔, CT 데이터 정합 자동화
Multi-modal Data 정합

54. Multi-Center Dataset for the Registration
▪ External Dataset
▪ CBCT acquired by another institution
▪ Paired CBCT-Full arch scan models
▪ Augmentation for Partially Scanned Dental Model
: 3,304 jaws
▪ Generated by cropping full arch model

55. Surface Distance

56. Registration Accuracy (External Validation)
▪ Average Surface Distance: 0.23 ± 0.14 mm
▪ Average Landmark Distance: 0.42 ± 0.20 mm
▪ Average Processing Time: 4.16 ± 0.55 s

58. 3Dme Studio
Features
- Multi-modality data alignment
- Alignment inspection
- CT data processing
- Coordinate control (to exocad & 3Shape)
- Boolean operation
Bellus3D 3D Facial Scanner

59. 3Dme Studio – www.3dme.ai

60. https://glidewelldental.com/solutions/glidewell-io/fastdesign-io-software-and-design-station

61. 딥러닝 기반 치아 모델 자동생성
3Dme Crown: 교합을 고려한 3D 치아 모델링 자동화 (GAN)
AI

62. 딥러닝 기반 치아 모델 자동생성
3Dme Crown: 교합을 고려한 3D 치아 모델링 자동화 (GAN)

63. 딥러닝 기반 치아 모델 자동생성
3Dme Crown: 교합을 고려한 3D 치아 모델링 자동화 (GAN)

64. 3Dme Crown
▪ AI & Cloud-based crown CAD
▪ Features
- Cloud-based project
- Automated (AI / Library) crown positioning
- Automated AI crown modeling
- Robust mesh handling

65. Internal validation: Training set / Validation set / Test set
Extra-validation: Multi-center Study
RCT (Randomized Controlled Trial) with RWD (Real World Data)
Possibility of Improvement: Transfer Learning, Continuous Learning
AI is only a tool to support human experts!
Efficacy in terms of Time & Cost
AI S/W Performance for Real World Data

66. Moravec’s Paradox
어려운 것은 쉽고 쉬운 것은 어렵다
컴퓨터에게 쉬운 것과 어려운 것
Easy Hard

67. 1900년도 뉴욕 1913년도 뉴욕
Q. 인공지능이 인간을 대체할 것인가?
“인간은 말과 경주하지 않는다.
말의 등에 올라타 이용하는 존재로서 인마일체(人馬一體)가 되는 게 중요하다.”
- 이어령 박사

68. www.imagoworks.ai www.3dme.ai
ceo@imagoworks.ai
Make people happier and healthier
with the best 3D medical solution