- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05348109
Assessment of Decision Support System Software in Extraction and Anchorage Planning Among Adult Patients Using Computer Algorithm
July 18, 2022 updated by: Walaa Mohamed Hassan Gadallah, Cairo University
It was introduced in dentistry to be used in innovative research and development in addition to facilitating the decision in complicated cases and ensure high patient care quality.
In the field of Orthodontics in specific, many studies previously mentioned the idea of artificial intelligence showing very promising results and high degree of reliability.
It was used in different domains in orthodontics like diagnosis, treatment planning, evaluation of treatment outcome
Study Overview
Status
Recruiting
Intervention / Treatment
Detailed Description
In this study, the aim is to access the efficiency of the new decision support system in determining whether the decision is extraction or non-extraction and the anchorage plan required for each case.
This was performed in the past in many countries and those studies are published
Study Type
Observational
Enrollment (Anticipated)
80
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Contact
- Name: Walaa Mohamed Gadallah, Bachelor degree
- Phone Number: 01021340189
- Email: walaa.hassan@dentistry.cu.edu.eg
Study Locations
-
-
-
Cairo, Egypt
- Recruiting
- Walaa Mohamed Hassan Gadallah
-
Contact:
- walaa mohamed Gadallah, Bachelor
- Phone Number: 01021340189
- Email: walaa.hassan@dentistry.cu.edu.eg
-
-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
15 years to 35 years (Child, Adult)
Accepts Healthy Volunteers
N/A
Genders Eligible for Study
All
Sampling Method
Non-Probability Sample
Study Population
Recruiting well finished cases having history of crowding from Kasr el Ainy with no severe skeletal discrepancy, cases should be well documented. The precise and complete documentation of the patients in terms of the presence of:
- Preoperative and postoperative x-rays.
- Good quality preoperative and postoperative photographs.
- Preoperative and postoperative study models.
- Detailed documentation of the treatment sequence and mechanics. If there were incomplete data provided in the patient's file; the patient will then be excluded from the study.
Description
Inclusion Criteria:
- Cases with well finished orthodontic treatment.
- Cases with history of crowding more than 10 mm and requiring extraction.
- Cases with no severe skeletal discrepancy.
- Well documented cases with both pre-operative and post-operative records.
- Patients with a full set of permanent teeth erupted
Exclusion Criteria:
- Improperly finished orthodontic cases.
- Cases with mild crowding managed by treatment options other than extraction.
- Growing patients or showing any residual growth remaining in cephalometric analysis
- Cases with severe skeletal discrepancy.
- Poorly documented cases.
- Patients not sticking to anchorage plan
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Other
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
well fininshed cases
|
To decide whether extraction or non-extraction decision will be made for each case
Other Names:
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
To study the efficiency of the program decisions in terms of extraction/non-extraction and Anchorage planning decisions
Time Frame: 1 year
|
The concordance correlation coefficient would be used to measure the agreement between the 2 methods on the basis of the values (%) assigned for each treatment option by the 2 methods (quantitative data).
|
1 year
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
- Mandava, P., Ganugapanta, V. R. and Pradesh, A. (2016) 'Review article Annals and Essences of Dentistry ANCHORAGE IN ORTHODONTICS : A LITERATURE REVIEW Review article', Annals and Essences of Dentistry, VIII(2).
- Muraev AA, Tsai P, Kibardin I, Oborotistov N, Shirayeva T, Ivanov S, Ivanov S, Guseynov N, Aleshina O, Bosykh Y, Safyanova E, Andreischev A, Rudoman S, Dolgalev A, Matyuta M, Karagodsky V, Tuturov N. Frontal cephalometric landmarking: humans vs artificial neural networks. Int J Comput Dent. 2020;23(2):139-148.
- Aksakalli S, Demir A, Selek M, Tasdemir S. Temperature increase during orthodontic bonding with different curing units using an infrared camera. Acta Odontol Scand. 2014 Jan;72(1):36-41. doi: 10.3109/00016357.2013.794954. Epub 2013 May 3.
- Auconi P, Scazzocchio M, Cozza P, McNamara JA Jr, Franchi L. Prediction of Class III treatment outcomes through orthodontic data mining. Eur J Orthod. 2015 Jun;37(3):257-67. doi: 10.1093/ejo/cju038. Epub 2014 Sep 4.
- Bichu YM, Hansa I, Bichu AY, Premjani P, Flores-Mir C, Vaid NR. Applications of artificial intelligence and machine learning in orthodontics: a scoping review. Prog Orthod. 2021 Jul 5;22(1):18. doi: 10.1186/s40510-021-00361-9. Review.
- Briganti G, Le Moine O. Artificial Intelligence in Medicine: Today and Tomorrow. Front Med (Lausanne). 2020 Feb 5;7:27. doi: 10.3389/fmed.2020.00027. eCollection 2020.
- Chen S, Wang L, Li G, Wu TH, Diachina S, Tejera B, Kwon JJ, Lin FC, Lee YT, Xu T, Shen D, Ko CC. Machine learning in orthodontics: Introducing a 3D auto-segmentation and auto-landmark finder of CBCT images to assess maxillary constriction in unilateral impacted canine patients. Angle Orthod. 2020 Jan;90(1):77-84. doi: 10.2319/012919-59.1. Epub 2019 Aug 12.
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- Choi HI, Jung SK, Baek SH, Lim WH, Ahn SJ, Yang IH, Kim TW. Artificial Intelligent Model With Neural Network Machine Learning for the Diagnosis of Orthognathic Surgery. J Craniofac Surg. 2019 Oct;30(7):1986-1989. doi: 10.1097/SCS.0000000000005650. Erratum in: J Craniofac Surg. 2020 Jun;31(4):1156.
- Dorsey ER, Glidden AM, Holloway MR, Birbeck GL, Schwamm LH. Teleneurology and mobile technologies: the future of neurological care. Nat Rev Neurol. 2018 May;14(5):285-297. doi: 10.1038/nrneurol.2018.31. Epub 2018 Apr 6. Review.
- Felfoul O, Mohammadi M, Taherkhani S, de Lanauze D, Zhong Xu Y, Loghin D, Essa S, Jancik S, Houle D, Lafleur M, Gaboury L, Tabrizian M, Kaou N, Atkin M, Vuong T, Batist G, Beauchemin N, Radzioch D, Martel S. Magneto-aerotactic bacteria deliver drug-containing nanoliposomes to tumour hypoxic regions. Nat Nanotechnol. 2016 Nov;11(11):941-947. doi: 10.1038/nnano.2016.137. Epub 2016 Aug 15.
- Feres M, Louzoun Y, Haber S, Faveri M, Figueiredo LC, Levin L. Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles. Int Dent J. 2018 Feb;68(1):39-46. doi: 10.1111/idj.12326. Epub 2017 Aug 2.
- Goto S, Kimura M, Katsumata Y, Goto S, Kamatani T, Ichihara G, Ko S, Sasaki J, Fukuda K, Sano M. Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients. PLoS One. 2019 Jan 9;14(1):e0210103. doi: 10.1371/journal.pone.0210103. eCollection 2019.
- Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017 Apr;69S:S36-S40. doi: 10.1016/j.metabol.2017.01.011. Epub 2017 Jan 11.
- Hwang JJ, Lee JH, Han SS, Kim YH, Jeong HG, Choi YJ, Park W. Strut analysis for osteoporosis detection model using dental panoramic radiography. Dentomaxillofac Radiol. 2017 Oct;46(7):20170006. doi: 10.1259/dmfr.20170006. Epub 2017 Jul 14.
- Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, Maganur PC, Patil S, Naik S, Baeshen HA, Sarode SS. Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic review. J Dent Sci. 2021 Jan;16(1):482-492. doi: 10.1016/j.jds.2020.05.022. Epub 2020 Jun 5. Review.
- Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics : Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop. 2020 Jan;81(1):52-68. doi: 10.1007/s00056-019-00203-8. Epub 2019 Dec 18.
- Yeom SH, Na JS, Jung HD, Cho HJ, Choi YJ, Lee JS. Computational analysis of airflow dynamics for predicting collapsible sites in the upper airways: machine learning approach. J Appl Physiol (1985). 2019 Oct 1;127(4):959-973. doi: 10.1152/japplphysiol.01033.2018. Epub 2019 Jul 18.
- Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod. 2010 Mar;80(2):262-6. doi: 10.2319/111608-588.1.
- Turakhia MP, Desai M, Hedlin H, Rajmane A, Talati N, Ferris T, Desai S, Nag D, Patel M, Kowey P, Rumsfeld JS, Russo AM, Hills MT, Granger CB, Mahaffey KW, Perez MV. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study. Am Heart J. 2019 Jan;207:66-75. doi: 10.1016/j.ahj.2018.09.002. Epub 2018 Sep 8.
- Topalovic M, Das N, Burgel PR, Daenen M, Derom E, Haenebalcke C, Janssen R, Kerstjens HAM, Liistro G, Louis R, Ninane V, Pison C, Schlesser M, Vercauter P, Vogelmeier CF, Wouters E, Wynants J, Janssens W; Pulmonary Function Study Investigators; Pulmonary Function Study Investigators:. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J. 2019 Apr 11;53(4). pii: 1801660. doi: 10.1183/13993003.01660-2018. Print 2019 Apr.
- Thanathornwong B. Bayesian-Based Decision Support System for Assessing the Needs for Orthodontic Treatment. Healthc Inform Res. 2018 Jan;24(1):22-28. doi: 10.4258/hir.2018.24.1.22. Epub 2018 Jan 31.
- Suhail Y, Upadhyay M, Chhibber A, Kshitiz. Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning. Bioengineering (Basel). 2020 Jun 12;7(2). pii: E55. doi: 10.3390/bioengineering7020055.
- Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018 Oct;77:106-111. doi: 10.1016/j.jdent.2018.07.015. Epub 2018 Jul 26.
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- Skotko BG, Macklin EA, Muselli M, Voelz L, McDonough ME, Davidson E, Allareddy V, Jayaratne YS, Bruun R, Ching N, Weintraub G, Gozal D, Rosen D. A predictive model for obstructive sleep apnea and Down syndrome. Am J Med Genet A. 2017 Apr;173(4):889-896. doi: 10.1002/ajmg.a.38137. Epub 2017 Jan 26.
- Patcas R, Timofte R, Volokitin A, Agustsson E, Eliades T, Eichenberger M, Bornstein MM. Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups. Eur J Orthod. 2019 Aug 8;41(4):428-433. doi: 10.1093/ejo/cjz007.
- Patcas R, Bernini DAJ, Volokitin A, Agustsson E, Rothe R, Timofte R. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg. 2019 Jan;48(1):77-83. doi: 10.1016/j.ijom.2018.07.010. Epub 2018 Aug 4.
- Li P, Kong D, Tang T, Su D, Yang P, Wang H, Zhao Z, Liu Y. Orthodontic Treatment Planning based on Artificial Neural Networks. Sci Rep. 2019 Feb 14;9(1):2037. doi: 10.1038/s41598-018-38439-w.
- Ma Q, Kobayashi E, Fan B, Nakagawa K, Sakuma I, Masamune K, Suenaga H. Automatic 3D landmarking model using patch-based deep neural networks for CT image of oral and maxillofacial surgery. Int J Med Robot. 2020 Jun;16(3):e2093. doi: 10.1002/rcs.2093. Epub 2020 Mar 20.
- Niño-Sandoval TC, Guevara Pérez SV, González FA, Jaque RA, Infante-Contreras C. Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III. Forensic Sci Int. 2017 Dec;281:187.e1-187.e7. doi: 10.1016/j.forsciint.2017.10.004. Epub 2017 Oct 12.
- Nieri M, Crescini A, Rotundo R, Baccetti T, Cortellini P, Pini Prato GP. Factors affecting the clinical approach to impacted maxillary canines: A Bayesian network analysis. Am J Orthod Dentofacial Orthop. 2010 Jun;137(6):755-62. doi: 10.1016/j.ajodo.2008.08.028.
- Nanda, R. (2012) 'Biomechanics and Esthetics Strategies in Clinical Orthodontics', Paper Knowledge . Toward a Media History of Documents, p. 194.
- Nahidh, M., Am, A. A. and Sc, A. (2019) 'Understanding Anchorage in Orthodontics', ARC Journal of Dental Science, 4(3). doi: 10.20431/2456-0030.0403002.
- Montúfar J, Romero M, Scougall-Vilchis RJ. Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. Am J Orthod Dentofacial Orthop. 2018 Jul;154(1):140-150. doi: 10.1016/j.ajodo.2017.08.028.
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- Martina, R. et al. (2006) 'Neural Network Based System for Decision Making Support in Orthodontic Extractions', Intelligent Production Machines and Systems - 2nd I*PROMS Virtual International Conference 3-14 July 2006, pp. 235-240. doi: 10.1016/B978-008045157-2/50045-6.
Helpful Links
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Anticipated)
August 22, 2022
Primary Completion (Anticipated)
November 1, 2022
Study Completion (Anticipated)
April 1, 2023
Study Registration Dates
First Submitted
December 21, 2021
First Submitted That Met QC Criteria
April 23, 2022
First Posted (Actual)
April 27, 2022
Study Record Updates
Last Update Posted (Actual)
July 20, 2022
Last Update Submitted That Met QC Criteria
July 18, 2022
Last Verified
July 1, 2022
More Information
Terms related to this study
Other Study ID Numbers
- 94030405
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Undecided
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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