Evaluation of deep learning and convolutional neural network algorithms accuracy for detecting and predicting anatomical landmarks on 2D lateral cephalometric images: A systematic review and meta-analysis

Londono, Jimmy; Ghasmi, Shohreh; Shah, Altaf; Fahimipour, Amir; Ghadimi, Niloofar; Hashemi, Seyedeh‐Sara; Khurshid, Zohaib; Dashti, Mahmood

doi:10.1016/j.sdentj.2023.05.014

Cited by 15 publications

(6 citation statements)

References 55 publications

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“…The synergistic utilization of artificial intelligence and FEA studies holds the potential to unlock valuable insights into the practical applications of dental implants. 37 38 39 40 41 Further long-term clinical studies to examine the effectiveness of NRC are needed.…”

Section: Discussionmentioning

confidence: 99%

Stress Analysis of Endodontically Treated Tooth–Implant Different Connectors Designs in Maxillary Posterior Region: A Finite Element Analysis

Hashemi,

Baghaei,

Fathi

et al. 2023

Eur J Dent

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Objectives Using finite element analysis (FEA), this study aimed to determine the effect of nonrigid connectors (NRCs) and their position on the success of tooth and implant-supported fixed prostheses in the maxillary posterior region. Materials and Methods Three three-dimensional FEA models were designed, presuming maxillary second premolar and first molar to be extracted. Implant (replacing first molar), abutment, bone (spongious and cortical), first premolar (containing dentin, root cement, gutta-percha, and casting post and core), periodontal ligament, and three three-unit cemented porcelain-fused-to-metal prostheses (a rigid one and two nonrigid) were modeled. The NRC was once on the tooth side and once on the implant side. The prostheses were loaded twice. The first molar (180 N) and premolars (120 N) teeth were subjected to progressive vertical and oblique (12-degree) loads, and maximum von Mises stress and strain in teeth and connectors were calculated for each model. Results The findings of the current study showed evidence that tooth-implant design with an NRC has significantly increased the average stress in the tooth. The average stress in dentin was 769.02 for the mesial connector and 766.95 for the distal connector, and this was only 731.59 for rigid connector. Furthermore, it was observed that rigid connector has considerably minimized the stress within the tooth–implant-supported fixed partial denture. The average stress for the crown and metal frame is 346.22 and 526.41 in rigid connector, while it is 1,172.9 and 2,050.9 for the nonrigid mesial connector. Conclusion Although distal NRC was more efficient than mesial NRC, using NRC will only reduce the stress applied to cortical bone and is not recommended in the posterior region of the maxilla.

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Section: Discussionmentioning

confidence: 99%

Stress Analysis of Endodontically Treated Tooth–Implant Different Connectors Designs in Maxillary Posterior Region: A Finite Element Analysis

Hashemi,

Baghaei,

Fathi

et al. 2023

Eur J Dent

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“…The simplicity of the technique, low risk and limited side effects have had beneficial therapeutic effects, and the injection technique is accepted by patients (18). There have been tremendous advancements in the field of artificial intelligence that can help detect anatomic landmarks (19), or help with oral diagnosing and dental treatment (20). In most cases, surgery seems to be an invasive method compared to the limited symptoms experienced by the patient.…”

Section: Discussionmentioning

confidence: 99%

Prevalence of signs and symptoms of temporomandibular joint disorders and associated factors among Iranian dental student

Maedeh Banki,

Ali Javaherian,

Parisa Tahmasebi

et al. 2023

World J. Bio. Pharm. Health Sci.

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Introduction: Temporomandibular disorders include a number of clinical signs and symptoms that involve the muscles, temporomandibular joint, and teeth. The aim of this study was to investigate the prevalence of signs and symptoms of temporomandibular joint disorders in dental students of Golestan University of Medical Sciences in the academic year 1400-1401. Method: In this study, 193 dental students were selected from the population under study by available sampling method. First, the necessary information was collected through interviews and completing the checklist. Male and female students were randomly selected and were subjected to a complete examination in terms of the health of the temporomandibular joint. After data entry in SPSS software version 19, the data was described with the help of frequency and percentage and chi-score test. A significance level of 0.05 was considered. Results: 193 students participated in this study, of which 98 were male and 95 were female. There were 30 first-year students, 30 second-year students, 39 third-year students, 32 fourth-year students, 26 fifth-year students, and 36 sixth-year students. The average age of the students was 24.5 years. Occlusion of class 1, class 2 and class 3 was seen in 156 people (80.8%), 29 people (15%) and 8 people (4.1%), respectively. A history of trauma to the jaw was reported in 14 people (7.3%). 17 people (8.8%) mentioned headache. Bruxism was observed in 13 people. The results of chi square test showed that there was no statistically significant relationship between related variables to TMJ disorder and gender. Conclusion: Considering the relatively high prevalence of temporomandibular joint disorder among dental students, further studies are recommended to investigate other related factors.

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Section: Cephalometric Analysismentioning

confidence: 99%

“…Meta-analyses have generally shown high accuracy in identifying cephalometric landmarks [83][84][85][86][87][88][89]. However, the results are strongly dependent on predefined thresholds, with lower accuracies reported at a 2 mm threshold [83,85,88]. Serafin et al [89] conducted a study in 2023 and reported a mean difference of 2.44 mm between three-dimensional (3D) automated and manual landmarking.…”

Section: Cephalometric Analysismentioning

confidence: 99%

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning—A Comprehensive Review

Kazimierczak,

Serafin

et al. 2024

JCM

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The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.

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Evaluation of deep learning and convolutional neural network algorithms accuracy for detecting and predicting anatomical landmarks on 2D lateral cephalometric images: A systematic review and meta-analysis

Cited by 15 publications

References 55 publications

Stress Analysis of Endodontically Treated Tooth–Implant Different Connectors Designs in Maxillary Posterior Region: A Finite Element Analysis

Stress Analysis of Endodontically Treated Tooth–Implant Different Connectors Designs in Maxillary Posterior Region: A Finite Element Analysis

Prevalence of signs and symptoms of temporomandibular joint disorders and associated factors among Iranian dental student

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning—A Comprehensive Review

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