Dental disease is the first complaint of 10 major diseases in Indonesia. Almost the entire population of Indonesia has dental and oral health problems. Delay in early detection of dental and oral health problems can result in loss of productivity, longer duration of treatment, loss of teeth, higher costs, complications with other organs, which can lead to death due to cancer. We propose the invention concerns wireless oral image recording instruments and identification methods thereof. Several existing inventions do not yet have the following features including wireless connection, object detection, and prediction of oral and dental diseases that integrate the knowledge of clinical experts using the application. The purpose of this invention was to add to the features mentioned above by using Artificial Intelligence of Medical Things (AIoMT) concept, designing object detection based on deep learning (DL) algorithms, and integrating prediction systems high performance using collaborative datasets. This invention integrated intraoral image recording hardware in the Intraoral Camera Unit (ICU) with software in the Display Processing Unit (DPU) which produces visualization of predicted results, and a webserver unit as a data collection unit and image classifier according to the validated disease interpretation. The integration of these three units can then be used by medical scholars, researchers and medical practitioners to strengthen the reliability of the interpretation process, especially related to oral diseases using an integrated digital dental support system.

1.
R. L.
Siegel
,
K. D.
Miller
, and
A.
Jemal
,
CA Cancer J. Clin.
70
,
7
30
, (
2020
).
2.
S. E.
Scott
,
E. A.
Grunfeld
, and
M.
McGurk
.
Community dentistry and oral epidemiology
34
,
337
343
(
2006
).
3.
F.
Ercal
,
A.
Chawla
,
W. V.
Stoecker
,
H. C.
Lee
, and
R. H.
Moss
,
IEEE Trans. Biomed. Eng.
41
,
837
845
(
1994
).
4.
P.
Schmid
,
IEEE Trans. Med. Imaging
18
,
164
171
(
1999
).
5.
R. B.
Oliveira
,
N.
Marranghello
,
A. S.
Pereira
, and
J. M. R. S.
Tavares
,
Expert Syst. Appl.
61
,
53
63
(
2016
).
6.
J.
Kawahara
and
G.
Hamarneh
, “
Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers
,” in
Machine Learning in Medical Imaging
,
7th International Conference on Machine Learning in Medical Imaging (MLMI 2016
), edited by
L.
Wang
, et al. (
Springer International Publishing
,
Athens
,
2016
), pp.
164
171
.
7.
A.
Creswell
,
A.
Pouplin
, and
A. A.
Bharath
,
IET Comput. Vis.
12
,
1105
1111
(
2018
).
8.
M. A.
Albahar
,
IEEE Access
7
,
38306
38313
(
2019
).
9.
C.
Sun
,
A.
Shrivastava
,
S.
Singh
, and
A.
Gupta
, “
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
,” in
International Conference on Computer Vision (ICCV)-2017
,
Proceeding IEEE Computer Society Digital Library
, edited by
L.
O’Conner
, et al (
IEEE Publisher
,
Venice
,
2017
), pp.
843
852
.
10.
A.
Rajkomar
,
J.
Dean
, and
I.
Kohane
,
N. Engl. J. Med.
380
,
1347
1358
(
2019
).
11.
J. R.
Zech
,
M. A.
Badgeley
,
M.
Liu
,
A. B.
Costa
,
J. J.
Titano
, and
E. K.
Oermann
,
PLOS Med.
15
,
e1002683
(
2018
).
12.
E. A.
AlBadawy
,
A.
Saha
, and
M. A.
Mazurowski
,
Med. Phys.
45
,
1150
1158
(
2018
).
13.
W. N.
Price
and
I. G.
Cohen
,
Nat. Med.
25
,
37
43
(
2019
).
14.
N.
Truong
,
K.
Sun
,
S.
Wang
,
F.
Guitton
, and
Y.
Guo
,
Comput. Secur.
110
,
102402
(
2021
).
15.
B.
McMahan
,
E.
Moore
,
D.
Ramage
,
S.
Hampson
, and
B.
Arcas
, “
Communication-Efficient Learning of Deep Networks from Decentralized Data
,” in
Artificial Intelligence and Statistics
,
20th International Conference on Artificial Intelligence and Statistics (AISTATS
), edited by
A.
Singh
, et al. (
PMLR Publisher
,
Florida
,
2017
),pp.
1273
1282
.
This content is only available via PDF.
You do not currently have access to this content.