Memetic Search Optimization Along with Genetic Scale Recurrent Neural Network for Predictive Rate of Implant Treatment

Alarifi, Abdulaziz; AlZubi, Ahmad Ali

doi:10.1007/s10916-018-1051-1

Cited by 13 publications

(9 citation statements)

References 14 publications

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“…Early and proper orthodontic intervention can prevent bone loss in the hypodontic areas and create root parallelism to facilitate implant insertion surgery [16]. The most common orthodontic strategy for bone and keratinized mucosa preservation include orthodontic extrusion of primary teeth in infra-occlusion, delayed orthodontic space opening, orthodontic gap closure in risky implant regions, and the use of a rigid retainer to maintain the implant sites [16,34,35,36,37].…”

Section: Discussionmentioning

confidence: 99%

Oral Rehabilitation of Hypodontia Patients Using an Endosseous Dental Implant: Functional and Aesthetic Results

Attia

Schaaf

Khassawna

et al. 2019

JCM

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Hypodontia often leads to limited bone availability of the alveolar ridges. Oral rehabilitation of severe hypodontia patients is challenging. In this retrospective study, we evaluated the functional and aesthetic results after dental implants in hypodontia patients, corroborated by Albrektsson implant success criteria. Over a period of 15 years (2000–2015), a total of 43 patients were diagnosed with hypodontia and 165 dental implants were inserted. Six patients who received 10 implants were lost in the follow-up. We examined 155 implants in 37 patients between December 2015 and May 2017. Besides family history, patients evaluated the general satisfaction, functionality, and aesthetics of the implants. Study subjects were between 17 and 44 years old (mean ± SD: 21.4 ± 5.6). Hypodontia patients were missing one to five teeth (n = 28), whereas patients diagnosed with oligodontia (≥6 missing teeth, n = 9). In this study, 24 patients (64.9%) with hypodontia had a positive family history; the remaining 13 patients had no family member with hypodontia. The final follow-up time ranged between 5 and 189 months after implant placement. Orthodontic treatment was performed in 32 patients (86%) before implant placement. Rehabilitation resulted in 62% of the cases being treated with 1–2 implants and 38% treated with 3–15 implants. However, out of 155 inserted dental implants, 18 implants failed to meet Albrektsson criteria, under which two implants were removed. Only autografts were used for bone augmentation with 97 implants. More than two-thirds of the patients showed high general satisfaction and masticatory function (69.4%) as well as phonetic ability (80.6%). The aesthetic outcome was rated as excellent by 17 patients (47.2%). The findings emphasize the importance of interdisciplinary treatment of hypodontia, leading to a satisfactory, functional, and long-term fixed prosthodontics using dental implants.

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Section: Discussionmentioning

confidence: 99%

Oral Rehabilitation of Hypodontia Patients Using an Endosseous Dental Implant: Functional and Aesthetic Results

Attia

Schaaf

Khassawna

et al. 2019

JCM

View full text Add to dashboard Cite

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“…By simultaneously analyzing the patients’ data, the implant system, and the surgeons’ operations, recurrent ANN (Fig. 2C) with memetic search optimization attained 99.2% accuracy in success rate prediction (Alarifi and AlZubi 2018). Second, AI was introduced as a surrogate to contemporary technology in predicting the mechanical performance of a bioimplant system, relieving high computational cost in optimizing implant design variables.…”

Section: Applications Of Ai In Dentistrymentioning

confidence: 99%

Application of Artificial Intelligence in Dentistry

2020

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Artificial intelligence (AI) is a technology that utilizes machines to mimic intelligent human behavior. To appreciate human-technology interaction in the clinical setting, augmented intelligence has been proposed as a cognitive extension of AI in health care, emphasizing its assistive and supplementary role to medical professionals. While truly autonomous medical robotic systems are still beyond reach, the virtual component of AI, known as software-type algorithms, is the main component used in dentistry. Because of their powerful capabilities in data analysis, these virtual algorithms are expected to improve the accuracy and efficacy of dental diagnosis, provide visualized anatomic guidance for treatment, simulate and evaluate prospective results, and project the occurrence and prognosis of oral diseases. Potential obstacles in contemporary algorithms that prevent routine implementation of AI include the lack of data curation, sharing, and readability; the inability to illustrate the inner decision-making process; the insufficient power of classical computing; and the neglect of ethical principles in the design of AI frameworks. It is necessary to maintain a proactive attitude toward AI to ensure its affirmative development and promote human-technology rapport to revolutionize dental practice. The present review outlines the progress and potential dental applications of AI in medical-aided diagnosis, treatment, and disease prediction and discusses their data limitations, interpretability, computing power, and ethical considerations, as well as their impact on dentists, with the objective of creating a backdrop for future research in this rapidly expanding arena.

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“…In order to verify the effectiveness and advantages of the proposed method, the experiment uses maize as the data set, extracts and fuses the features of miRNA and lncRNA according to the method in Section 4.3, and uses Naive Bayes (NB) [26], gradient boosting decision tree (GBDT) [27], random forest (RF) [28] and decision tree (DT) [29] methods for classification prediction, comparative experiments, 5-fold cross-check [30]. The experimental results are shown in Tab.…”

Section: Comparative Analysismentioning

confidence: 99%

An Optimized Technique for RNA Prediction Based on Neural Network

AlZubi¹,

Alanazi²

2023

Intelligent Automation &Amp; Soft Computing

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Pathway reconstruction, which remains a primary goal for many investigations, requires accurate inference of gene interactions and causality. Non-coding RNA (ncRNA) is studied because it has a significant regulatory role in many plant and animal life activities, but interacting micro-RNA (miRNA) and long non-coding RNA (lncRNA) are more important. Their interactions not only aid in the in-depth research of genes' biological roles, but also bring new ideas for illness detection and therapy, as well as plant genetic breeding. Biological investigations and classical machine learning methods are now used to predict miRNA-lncRNA interactions. Because biological identification is expensive and time-consuming, machine learning requires too much manual intervention, and the feature extraction process is difficult. This research presents a deep learning model that combines the advantages of convolutional neural networks (CNN) and bidirectional long short-term memory networks (Bi-LSTM). It not only takes into account the connection of information between sequences and incorporates contextual data, but it also thoroughly extracts the sequence data's features. On the corn data set, cross-checking is used to evaluate the model's performance, and it is compared to classical machine learning. To acquire a superior classification effect, the proposed strategy was compared to a single model. Additionally, the potato and wheat data sets were utilized to evaluate the model, with accuracy rates of 95% and 93%, respectively, indicating that the model had strong generalization capacity.

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Memetic Search Optimization Along with Genetic Scale Recurrent Neural Network for Predictive Rate of Implant Treatment

Cited by 13 publications

References 14 publications

Oral Rehabilitation of Hypodontia Patients Using an Endosseous Dental Implant: Functional and Aesthetic Results

Oral Rehabilitation of Hypodontia Patients Using an Endosseous Dental Implant: Functional and Aesthetic Results

Application of Artificial Intelligence in Dentistry

An Optimized Technique for RNA Prediction Based on Neural Network

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