PMID- 35741232
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220716
IS  - 2075-4418 (Print)
IS  - 2075-4418 (Electronic)
IS  - 2075-4418 (Linking)
VI  - 12
IP  - 6
DP  - 2022 Jun 9
TI  - Diagnosis of Tooth Prognosis Using Artificial Intelligence.
LID - 10.3390/diagnostics12061422 [doi]
LID - 1422
AB  - The accurate diagnosis of individual tooth prognosis has to be determined 
      comprehensively in consideration of the broader treatment plan. The objective of 
      this study was to establish an effective artificial intelligence (AI)-based 
      module for an accurate tooth prognosis decision based on the Harvard School of 
      Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The 
      tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels 
      (1-Hopeless, 5-Good condition for long term) by two groups (Model-A with 16, and 
      Model-B with 13 examiners) based on 17 clinical determining factors selected from 
      the HSDM-CTPC. Three AI machine-learning methods including gradient boosting 
      classifier, decision tree classifier, and random forest classifier were used to 
      create an algorithm. These three methods were evaluated against the gold standard 
      data determined by consensus of three experienced prosthodontists, and their 
      accuracy was analyzed. The decision tree classifier indicated the highest 
      accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient 
      boosting classifier and the random forest classifier was 0.6896, 0.6687, and 
      0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best 
      accuracy among the three methods. The study contributes to the implementation of 
      AI in the decision-making process of tooth prognosis in consideration of the 
      treatment plan.
FAU - Lee, Sang J
AU  - Lee SJ
AUID- ORCID: 0000-0001-7142-2890
AD  - Department of Restorative Dentistry and Biomaterial Sciences, Harvard School of 
      Dental Medicine, Boston, MA 02115, USA.
FAU - Chung, Dahee
AU  - Chung D
AD  - Harvard School of Dental Medicine, Boston, MA 02115, USA.
FAU - Asano, Akiko
AU  - Asano A
AD  - Department of Restorative Dentistry, School of Dental Medicine, Iwate Medical 
      University, Morioka 020-8505, Japan.
FAU - Sasaki, Daisuke
AU  - Sasaki D
AD  - Department of Periodontology, School of Dental Medicine, Iwate Medical 
      University, Morioka 020-8505, Japan.
FAU - Maeno, Masahiko
AU  - Maeno M
AD  - Department of Adhesive Dentistry, School of Life Dentistry at Tokyo, The Nippon 
      Dental University, Chiyoda-ku, Tokyo 102-8159, Japan.
FAU - Ishida, Yoshiki
AU  - Ishida Y
AUID- ORCID: 0000-0002-8686-3215
AD  - Department of Dental Materials Science, School of Life Dentistry at Tokyo, The 
      Nippon Dental University, Chiyoda-ku, Tokyo 102-8159, Japan.
FAU - Kobayashi, Takuya
AU  - Kobayashi T
AD  - Department of Oral Rehabilitation, School of Dental Medicine, Iwate Medical 
      University, Morioka 020-8505, Japan.
FAU - Kuwajima, Yukinori
AU  - Kuwajima Y
AD  - Department of Orthodontics, School of Dental Medicine, Iwate Medical University, 
      Morioka 020-8505, Japan.
FAU - Da Silva, John D
AU  - Da Silva JD
AUID- ORCID: 0000-0002-3341-6681
AD  - Department of Restorative Dentistry and Biomaterial Sciences, Harvard School of 
      Dental Medicine, Boston, MA 02115, USA.
FAU - Nagai, Shigemi
AU  - Nagai S
AD  - Department of Oral Medicine, Infection and Immunity, Harvard School of Dental 
      Medicine, Boston, MA 02115, USA.
LA  - eng
PT  - Journal Article
DEP - 20220609
PL  - Switzerland
TA  - Diagnostics (Basel)
JT  - Diagnostics (Basel, Switzerland)
JID - 101658402
PMC - PMC9221626
OTO - NOTNLM
OT  - artificial intelligence (AI)
OT  - diagnosis
OT  - machine learning
OT  - prosthodontics
OT  - tooth prognosis
OT  - treatment plan
COIS- The authors declare no conflict of interest.
EDAT- 2022/06/25 06:00
MHDA- 2022/06/25 06:01
PMCR- 2022/06/09
CRDT- 2022/06/24 01:10
PHST- 2022/05/24 00:00 [received]
PHST- 2022/06/06 00:00 [revised]
PHST- 2022/06/07 00:00 [accepted]
PHST- 2022/06/24 01:10 [entrez]
PHST- 2022/06/25 06:00 [pubmed]
PHST- 2022/06/25 06:01 [medline]
PHST- 2022/06/09 00:00 [pmc-release]
AID - diagnostics12061422 [pii]
AID - diagnostics-12-01422 [pii]
AID - 10.3390/diagnostics12061422 [doi]
PST - epublish
SO  - Diagnostics (Basel). 2022 Jun 9;12(6):1422. doi: 10.3390/diagnostics12061422.