Assessment of Decision Support System Software in Extraction and Anchorage Planning Among Adult Patients Using Computer Algorithm

NCT ID: NCT05348109

Last Updated: 2022-07-20

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Total Enrollment

80 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-08-22

Study Completion Date

2023-04-30

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Cases That Need Extraction of Teeth in Orthodontics Anchorage Planning

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

OTHER

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

well fininshed cases

Well finished cases

Intervention Type OTHER

To decide whether extraction or non-extraction decision will be made for each case

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Well finished cases

To decide whether extraction or non-extraction decision will be made for each case

Intervention Type OTHER

Other Intervention Names

Discover alternative or legacy names that may be used to describe the listed interventions across different sources.

extraction and non-extraction in well finished cases

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

1. Cases with well finished orthodontic treatment.
2. Cases with history of crowding more than 10 mm and requiring extraction.
3. Cases with no severe skeletal discrepancy.
4. Well documented cases with both pre-operative and post-operative records.
5. Patients with a full set of permanent teeth erupted

Exclusion Criteria

1. Improperly finished orthodontic cases.
2. Cases with mild crowding managed by treatment options other than extraction.
3. Growing patients or showing any residual growth remaining in cephalometric analysis
4. Cases with severe skeletal discrepancy.
5. Poorly documented cases.
6. Patients not sticking to anchorage plan
Minimum Eligible Age

15 Years

Maximum Eligible Age

35 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

Cairo University

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Walaa Mohamed Hassan Gadallah

Orthodontics Master candidate

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Walaa Mohamed Hassan Gadallah

Cairo, , Egypt

Site Status RECRUITING

Countries

Review the countries where the study has at least one active or historical site.

Egypt

Central Contacts

Reach out to these primary contacts for questions about participation or study logistics.

Walaa Mohamed Gadallah, Bachelor degree

Role: CONTACT

01021340189

Facility Contacts

Find local site contact details for specific facilities participating in the trial.

walaa mohamed Gadallah, Bachelor

Role: primary

01021340189

References

Explore related publications, articles, or registry entries linked to this study.

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).

Reference Type BACKGROUND

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.

Reference Type BACKGROUND
PMID: 32555767 (View on PubMed)

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.

Reference Type RESULT
PMID: 23638766 (View on PubMed)

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.

Reference Type RESULT
PMID: 25190642 (View on PubMed)

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.

Reference Type RESULT
PMID: 34219198 (View on PubMed)

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.

Reference Type RESULT
PMID: 32118012 (View on PubMed)

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.

Reference Type RESULT
PMID: 31403836 (View on PubMed)

Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int. 2020;51(3):248-257. doi: 10.3290/j.qi.a43952.

Reference Type RESULT
PMID: 32020135 (View on PubMed)

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.

Reference Type RESULT
PMID: 31205280 (View on PubMed)

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.

Reference Type RESULT
PMID: 29623949 (View on PubMed)

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.

Reference Type RESULT
PMID: 27525475 (View on PubMed)

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.

Reference Type RESULT
PMID: 28771699 (View on PubMed)

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.

Reference Type RESULT
PMID: 30625197 (View on PubMed)

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.

Reference Type RESULT
PMID: 28126242 (View on PubMed)

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.

Reference Type RESULT
PMID: 28707523 (View on PubMed)

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.

Reference Type RESULT
PMID: 33384838 (View on PubMed)

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.

Reference Type RESULT
PMID: 31853586 (View on PubMed)

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.

Reference Type RESULT
PMID: 31318618 (View on PubMed)

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.

Reference Type RESULT
PMID: 19905850 (View on PubMed)

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.

Reference Type RESULT
PMID: 30392584 (View on PubMed)

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):1801660. doi: 10.1183/13993003.01660-2018. Print 2019 Apr.

Reference Type RESULT
PMID: 30765505 (View on PubMed)

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.

Reference Type RESULT
PMID: 29503749 (View on PubMed)

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):55. doi: 10.3390/bioengineering7020055.

Reference Type RESULT
PMID: 32545428 (View on PubMed)

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.

Reference Type RESULT
PMID: 30056118 (View on PubMed)

Lee KS, Jung SK, Ryu JJ, Shin SW, Choi J. Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs. J Clin Med. 2020 Feb 1;9(2):392. doi: 10.3390/jcm9020392.

Reference Type RESULT
PMID: 32024114 (View on PubMed)

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.

Reference Type RESULT
PMID: 28124477 (View on PubMed)

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.

Reference Type RESULT
PMID: 30788496 (View on PubMed)

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.

Reference Type RESULT
PMID: 30087062 (View on PubMed)

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.

Reference Type RESULT
PMID: 30765756 (View on PubMed)

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.

Reference Type RESULT
PMID: 32065718 (View on PubMed)

Nino-Sandoval TC, Guevara Perez SV, Gonzalez 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.

Reference Type RESULT
PMID: 29126697 (View on PubMed)

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.

Reference Type RESULT
PMID: 20685530 (View on PubMed)

Nanda, R. (2012) 'Biomechanics and Esthetics Strategies in Clinical Orthodontics', Paper Knowledge . Toward a Media History of Documents, p. 194.

Reference Type RESULT

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.

Reference Type RESULT

Montufar 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.

Reference Type RESULT
PMID: 29957312 (View on PubMed)

Mitchell, L. (2017) 'Introduction to Orthodontics', in Introduction to Orthodontics. 4th edn, pp. 179-190.

Reference Type RESULT

Mendes RG, de Souza CR, Machado MN, Correa PR, Di Thommazo-Luporini L, Arena R, Myers J, Pizzolato EB, Borghi-Silva A. Predicting reintubation, prolonged mechanical ventilation and death in post-coronary artery bypass graft surgery: a comparison between artificial neural networks and logistic regression models. Arch Med Sci. 2015 Aug 12;11(4):756-63. doi: 10.5114/aoms.2015.48145. Epub 2015 Aug 11.

Reference Type RESULT
PMID: 26322087 (View on PubMed)

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.

Reference Type RESULT

Provided Documents

Download supplemental materials such as informed consent forms, study protocols, or participant manuals.

Document Type: Study Protocol

View Document

Related Links

Access external resources that provide additional context or updates about the study.

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

94030405

Identifier Type: -

Identifier Source: org_study_id

More Related Trials

Additional clinical trials that may be relevant based on similarity analysis.