Development of an Artificial Intelligence System to Estimate Postoperative Discomfort After Impacted Third Molar Surgery
Abstract
Background: Artificial Neural Network (ANN) is relatively crude electronic model
based on the neural structure of human brain which was used in the field of
medicine in different purposes. It can be used for
many medical branches especially for estimating
the course of a certain disorder or treatment procedure. The aim of this
study is to use ANN in maxillofacial surgery to estimate the postoperative
symptoms after third molar surgery.
Methods:The pre and post-operative information of 175 consecutive patients who needed extraction of impacted third
molar teeth were employed to train an ANN. After the training
process, the information of 26 cases was
used in order to verify the network's ability to predict the post-operative
symptoms such as swelling, pain, decrease
of mouth opening, bleeding, number of days
to return to normal activities and duration of activity restriction. The results obtained from ANN were compared with
the results of patients self-reported information. The correlation between the postoperative symptoms of the patients and
outcomes obtained from the ANN were analyzed statistically.
Results: Close association was
found between the patients’ reports and ANN
results on post-operative pain, swelling, bleeding, number of days to return to normal
activities and duration of activity restriction.
Conclusions: The proposed ANN approach is easy to
implement and adapted to predict the
response of the postoperative outcomes.
The model can be further extended to include more variables and experimental
data to increase reliability.
Keywords:Activity restriction, artificial
neural network, postoperative discomfort, third molar surgery.
Keywords
activity restriction artificial neural network postoperative discomfort third molar surgery
References
- 1. Susarla SM, Blaeser BF, Magalnick D. Third molar surgery and associated complications. Oral Maxillofacial Surg Clin N Am 2003;15:177-86.2. Baqain ZH, Karaky AA, Sawair F, Khraisat A, Duaibis R, Rajab LD. Frequency estimates and risk factors for postoperative morbidity after third molar removal: a prospective cohort study. J Oral Maxillofac Surg 2008;66:2276-83.3. Dayhoff JE, De Leo JM. Artificial Neural Networks Opening the Black Box. Cancer 2001;91:1615-35.4. Brickley MR, Shepherd JP. Comparisons of the abilities of a neural network and three consultant oral surgeons to make decisions about third molar removal. Br Dent J 1997;182:59-63.5. Bui CH, Seldin EB, Dodson TB. Types, frequencies, and risk factors for complications after third molar extraction. J Oral Maxillofac Surg 2003;61:1379-89.6. Jerjes W, El-Maaytah M, Swinson B, Banu B, Upile T, D'Sa S, et al. Experience versus complication rate in third molar surgery. Head Face Med 2006;2:14.7. Kim JC, Choi SS, Wang SJ, Kim SG. Minor complications after third molar surgery: Type, incidence, and possible prevention. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2006;102:4-11.8. Blondeau F, Daniel NG. Extraction of impacted mandibular third molars: postoperative complications and their risk factors. J Can Dent Assoc 2007;73:325.9. Figueiredo R, Valmaseda-Castellón E, Laskin DM, et al Berini-Aytés L, Gay-Escoda C. Treatment of delayed-onset infections after impacted lower third molar extractions. J Oral Maxillofac Surg 2008;66:943-7.10. Kunkel M, Morbach T, Kleis W, Wagner W. Third molar complications requiring hospitalization. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2006;102:300-6.11. Susarla SM, Dodson TB. Risk factors for third molar extraction difficulty. J Oral Maxillofac Surg 2004;62:1363-71.12. Sittitavornwong, S, Waite PD, Holmes JD, Klapow JC. The necessity of routine clinic follow-up visits after third molar removal. J Oral Maxillofac Surg 2005;63:1278-82.13. Lago-Me´ndez L, Diniz-FreitasM, SenraRivera C, Gude-Sampedro F, Gándara Rey JM, García-García A. Relationships between surgical difficulty and postoperative pain in lower third molar extractions. J Oral Maxillofac Surg 2007;65:979–83.14. Rana M, Gellrich NC, Ghassemi A, Gerressen M, Riediger D, Modabber A. Three-dimensional evaluation of postoperative swelling after third molar surgery using 2 different cooling therapy methods: a randomized observer-blind prospective study. J Oral Maxillofac Surg 2011;69:2092-8.15. Patel JL, Goyal RK. Applications of artificial neural networks in medical science. Curr Clin Pharmacol 2007;2:217-26.16. Mohammadfam I, Soltanzadeh A, Moghimbeigi A, Savareh BA. Use of artificial neural networks (anns) for the analysis and modeling of factors that affect occupational injuries in large construction industries. Electron Physician 2015;7:1515-22.
Gömülü Üçüncü Molar Cerrahisinden Sonra Postoperatif Rahatsızlığı Tahmin Etmek İçin Yapay Zeka Sisteminin Geliştirilmesi
Abstract
Amaç: Yapay Sinir Ağı (YSA), tıp alanında farklı amaçlar için kullanılan
nispeten insan beyninin sinir yapısına dayanan ham elektronik modeldir.
Özellikle belirli bir hastalığın seyrini veya tedavi prosedürünü tahmin etmek
için birçok tıp dalında kullanılabilmektedir. Bu çalışmanın amacı, üçüncü molar
cerrahisinden sonra postoperatif semptomları tahmin etmek için maksillofasiyal
cerrahide YSA kullanmaktır.
Gereç ve Yöntemler: Gömülü üçüncü molar dişleri çekilmesi gereken ardışık 175 hastanın
ameliyat öncesi ve sonrası bilgileri bir YSA'yı eğitmek için kullanıldı. Eğitim
sürecinin ardından; şişme, ağrı, ağız açıklığında azalma, kanama, normal
aktiviteye dönme gün sayısı ve aktivite kısıtlama süresi gibi postoperatif
semptomları öngörme yeteneğini doğrulamak için 26 vakanın bilgileri
kullanılmıştır. YSA'dan elde edilen sonuçlar, hastaların kendi rapor ettiği
bilgilerin sonuçlarıyla karşılaştırıldı. Postoperatif hastaların semptomları
ile YSA'dan elde edilen sonuçlar arasındaki korelasyon istatistiksel olarak
analiz edildi.
Bulgular: Ameliyat sonrası ağrı, şişme, kanama, normal aktivitelere dönme gün
sayısı ve aktivite kısıtlama süresi üzerine hastaların raporları ile YSA
sonuçları arasında yakın ilişki bulundu.
Sonuç: Önerilen YSA yaklaşımının, ameliyat sonrası sonuçların yanıtını
öngörmek için uygulanması kolay ve uygulanabilirdir. Model, güvenilirliği artırmak
için daha fazla değişken ve deneysel veri içerecek şekilde genişletilebilir.
Anahtar kelimeler: Aktivite kısıtlaması, üçüncü molar cerrahisi, postoperatif rahatsızlık,
yapay sinir ağı.
References
- 1. Susarla SM, Blaeser BF, Magalnick D. Third molar surgery and associated complications. Oral Maxillofacial Surg Clin N Am 2003;15:177-86.2. Baqain ZH, Karaky AA, Sawair F, Khraisat A, Duaibis R, Rajab LD. Frequency estimates and risk factors for postoperative morbidity after third molar removal: a prospective cohort study. J Oral Maxillofac Surg 2008;66:2276-83.3. Dayhoff JE, De Leo JM. Artificial Neural Networks Opening the Black Box. Cancer 2001;91:1615-35.4. Brickley MR, Shepherd JP. Comparisons of the abilities of a neural network and three consultant oral surgeons to make decisions about third molar removal. Br Dent J 1997;182:59-63.5. Bui CH, Seldin EB, Dodson TB. Types, frequencies, and risk factors for complications after third molar extraction. J Oral Maxillofac Surg 2003;61:1379-89.6. Jerjes W, El-Maaytah M, Swinson B, Banu B, Upile T, D'Sa S, et al. Experience versus complication rate in third molar surgery. Head Face Med 2006;2:14.7. Kim JC, Choi SS, Wang SJ, Kim SG. Minor complications after third molar surgery: Type, incidence, and possible prevention. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2006;102:4-11.8. Blondeau F, Daniel NG. Extraction of impacted mandibular third molars: postoperative complications and their risk factors. J Can Dent Assoc 2007;73:325.9. Figueiredo R, Valmaseda-Castellón E, Laskin DM, et al Berini-Aytés L, Gay-Escoda C. Treatment of delayed-onset infections after impacted lower third molar extractions. J Oral Maxillofac Surg 2008;66:943-7.10. Kunkel M, Morbach T, Kleis W, Wagner W. Third molar complications requiring hospitalization. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2006;102:300-6.11. Susarla SM, Dodson TB. Risk factors for third molar extraction difficulty. J Oral Maxillofac Surg 2004;62:1363-71.12. Sittitavornwong, S, Waite PD, Holmes JD, Klapow JC. The necessity of routine clinic follow-up visits after third molar removal. J Oral Maxillofac Surg 2005;63:1278-82.13. Lago-Me´ndez L, Diniz-FreitasM, SenraRivera C, Gude-Sampedro F, Gándara Rey JM, García-García A. Relationships between surgical difficulty and postoperative pain in lower third molar extractions. J Oral Maxillofac Surg 2007;65:979–83.14. Rana M, Gellrich NC, Ghassemi A, Gerressen M, Riediger D, Modabber A. Three-dimensional evaluation of postoperative swelling after third molar surgery using 2 different cooling therapy methods: a randomized observer-blind prospective study. J Oral Maxillofac Surg 2011;69:2092-8.15. Patel JL, Goyal RK. Applications of artificial neural networks in medical science. Curr Clin Pharmacol 2007;2:217-26.16. Mohammadfam I, Soltanzadeh A, Moghimbeigi A, Savareh BA. Use of artificial neural networks (anns) for the analysis and modeling of factors that affect occupational injuries in large construction industries. Electron Physician 2015;7:1515-22.
Details
| Primary Language | English |
|---|---|
| Subjects | Dentistry |
| Journal Section | Research |
| Authors | |
| Publication Date | August 1, 2020 |
| Submission Date | March 4, 2019 |
| Published in Issue | Year 2020 Volume: 7 Issue: 2 |
Selcuk Dental Journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY NC).