Мақаланы E-mail арқылы жіберу A neural network for clinical decision support in orthopedic dentistry
- Авторлар: Ignatov P.M.1, Oleynikov A.A.1, Gus'kov A.V.1, Shlykova A.L.1, Surov D.A.2
-
Мекемелер:
- Ryazan State Medical University
- “Denta Style Kanishchevo” LLC
- Шығарылым: Том 5, № 1S (2024)
- Беттер: 146-148
- Бөлім: Articles by YOUNG SCIENTISTS
- ##submission.dateSubmitted##: 15.02.2024
- ##submission.dateAccepted##: 05.03.2024
- ##submission.datePublished##: 03.07.2024
- URL: https://jdigitaldiagnostics.com/DD/article/view/627046
- DOI: https://doi.org/10.17816/DD627046
- ID: 627046
Дәйексөз келтіру
Толық мәтін
Аннотация
BACKGROUND: Artificial intelligence software used in contemporary dentistry is capable of autonomously selecting prosthetic structures based on treatment conditions, establishing a diagnosis based on X-ray and intraoral jaw scanning data. A neural network in the field of machine learning is a mathematical model that employs the principles of a neural network found in living organisms. It is capable of processing input signals in accordance with weight coefficients, passing them through a specific number of layers, and forming the correct answer at the output. This answer corresponds to the neuron of the output layer with the highest value of the activation function.
AIM: The aim of the study was to develop a neural network for clinical decision making in orthopedic treatment planning.
MATERIALS AND METHODS: A neural network was constructed using the Processing programming environment and a C-like programming language. At the stage of network training, the number of hidden layers was determined, the training coefficient was selected, and the number of training epochs was determined. The network was trained using the backpropagation of error method, which involved calculating the root-mean-square error of the network, backpropagating the signal through the neural network, and adjusting the weighting coefficients in consideration of the learning coefficient.
The input layer (vector) comprised clinical conditions [1, 2]: oral cavity condition, allergoanamnesis, and various manifestations of the clinical picture (index of destruction of tooth surfaces, vitality of teeth, etc.). The dimensionality of the output layer was dependent on the number of constructions used and amounted to 19 neurons (prostheses including burette, telescopic, cover, plate; microprostheses by type such as table-top, overlay, and inlay).
The output layer consisted of removable and fixed prostheses, the selection of which was based on a pre-designed algorithm. This algorithm was based on the following clinical conditions:
- Condition and number of teeth retained
- Index of destruction of the occlusal surface of masticatory teeth
- Black’s classification of carious cavities
- Parafunctions, allergic history [3, 4].
RESULTS: A neural network algorithm was developed in which a physician was required to input clinical data following an oral examination. The neural network, which facilitates clinical decision-making assistance, performs mathematical calculations in each layer, multiplying the elements of the input vector (and subsequently, each layer) by weighting coefficients (obtained as a result of training the neural network), and adding a bias. In order to obtain the results in the area of the activation function calculation, the obtained result was conducted through the activation function (Sigmoid, ReLu), selecting the output neuron with the largest result and predicting the most appropriate design [5, 6].
CONCLUSIONS: Consequently, the developed neural network is capable of proposing clinically justified variations of orthopedic treatment plans in individual cases, taking into account the potential use of different prostheses.
Толық мәтін
BACKGROUND: Artificial intelligence software used in contemporary dentistry is capable of autonomously selecting prosthetic structures based on treatment conditions, establishing a diagnosis based on X-ray and intraoral jaw scanning data. A neural network in the field of machine learning is a mathematical model that employs the principles of a neural network found in living organisms. It is capable of processing input signals in accordance with weight coefficients, passing them through a specific number of layers, and forming the correct answer at the output. This answer corresponds to the neuron of the output layer with the highest value of the activation function.
AIM: The aim of the study was to develop a neural network for clinical decision making in orthopedic treatment planning.
MATERIALS AND METHODS: A neural network was constructed using the Processing programming environment and a C-like programming language. At the stage of network training, the number of hidden layers was determined, the training coefficient was selected, and the number of training epochs was determined. The network was trained using the backpropagation of error method, which involved calculating the root-mean-square error of the network, backpropagating the signal through the neural network, and adjusting the weighting coefficients in consideration of the learning coefficient.
The input layer (vector) comprised clinical conditions [1, 2]: oral cavity condition, allergoanamnesis, and various manifestations of the clinical picture (index of destruction of tooth surfaces, vitality of teeth, etc.). The dimensionality of the output layer was dependent on the number of constructions used and amounted to 19 neurons (prostheses including burette, telescopic, cover, plate; microprostheses by type such as table-top, overlay, and inlay).
The output layer consisted of removable and fixed prostheses, the selection of which was based on a pre-designed algorithm. This algorithm was based on the following clinical conditions:
- Condition and number of teeth retained
- Index of destruction of the occlusal surface of masticatory teeth
- Black’s classification of carious cavities
- Parafunctions, allergic history [3, 4].
RESULTS: A neural network algorithm was developed in which a physician was required to input clinical data following an oral examination. The neural network, which facilitates clinical decision-making assistance, performs mathematical calculations in each layer, multiplying the elements of the input vector (and subsequently, each layer) by weighting coefficients (obtained as a result of training the neural network), and adding a bias. In order to obtain the results in the area of the activation function calculation, the obtained result was conducted through the activation function (Sigmoid, ReLu), selecting the output neuron with the largest result and predicting the most appropriate design [5, 6].
CONCLUSIONS: Consequently, the developed neural network is capable of proposing clinically justified variations of orthopedic treatment plans in individual cases, taking into account the potential use of different prostheses.
Авторлар туралы
Pavel Ignatov
Ryazan State Medical University
Хат алмасуға жауапты Автор.
Email: pavel08122002@yandex.ru
ORCID iD: 0009-0009-6326-3194
Ресей, Ryazan
Aleksandr Oleynikov
Ryazan State Medical University
Email: bandprod@yandex.ru
ORCID iD: 0000-0002-2245-1051
SPIN-код: 5579-5202
Ресей, Ryazan
Aleksandr Gus'kov
Ryazan State Medical University
Email: guskov74@gmail.com
ORCID iD: 0000-0001-9612-0784
SPIN-код: 3758-6378
Ресей, Ryazan
Alina Shlykova
Ryazan State Medical University
Email: shlykova.lina@bk.ru
ORCID iD: 0009-0001-7963-203X
Ресей, Ryazan
Dmitrii Surov
“Denta Style Kanishchevo” LLC
Email: surovda@gmail.com
ORCID iD: 0009-0008-5621-272X
Ресей, Ryazan
Әдебиет тізімі
- Ermolaeva PA. Comparison of thermoplastics and acrylic plastics for removable prosthetics. Nauchnoe obozrenie. Meditsinskie nauki. 2017;(4):16–20. EDN: YFVNZV
- Tatsenko EG, Lapina NV, Skorikova LA. Predicting patients' adaptation to removable dental structures. Mezhdunarodnyi zhurnal prikladnykh i fundamental'nykh issledovanii. 2017;(2):182–188. (In Russ).
- Tyan AA. The advantage of thermoplastic materials in prosthetic dentistry. Nauchnoe obozrenie. Meditsinskie nauki. 2017;(4):119–123. EDN: YFVOHN
- Rubtsova EA, Chirkova NV, Polushkina NA, et al. Evaluation of the microbiological examination of removable dentures of thermoplastic material. Journal of new medical technologies. 2017;(2):267–270. EDN: ZBADWD
- Dolgalev A, Muraev A, Lyakhov P, et al. Artificial intelligence architectonics and prospects for the application of machine learning technologies in dentistry. Literature review. Glavnyi vrach uga Russia. 2022;(5(86)). EDN: VSGWMU
- De Angelis F., Pranno N., Franchina A., et al. Artificial Intelligence: A New Diagnostic Software in Dentistry: A Preliminary Performance Diagnostic Study. International Journal of Environmental Research and Public Health. 2022;19(3):1728. doi: 10.3390/ijerph19031728
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