Incidence and pattern of implant fractures: A long‐term follow‐up multicenter study

Kim

Clinical Oral Implants Res

et al. 2019

Self Cite

Objectives This study investigated fracture rates and risk indicators for fractures in internal connection dental implants. Material and Methods We performed a retrospective analysis of 19,006 internal connection implants used in fixed restoration in 5,124 patients (4,570 males, 554 females) at the Dental Hospital of Veterans Health Service Medical Center between 2006 and 2015. Patients were followed through June 2018 (0.03–12.39 years post‐installation). Clinical factors (age, sex, implant diameter, implant length, placement site, bone graft, fixture material, cervical feature, abutment connection, microthread, and platform switching) were recorded. Kaplan–Meier survival analysis identified risk indicators associated with an implant fracture. Cox regression models elucidated potential fracture risks. Results One hundred and seventy‐four implants fractured in 135 patients, for an incidence rate of 0.92% after an average of 4.95 ± 2.14 years of use. Kaplan–Meier estimates showed that the 3‐, 5‐, and 10‐year survival rates of implants were 99.8%, 99.2%, and 97.7%, respectively. In the multivariable Cox regression model, the diameter, location, history of bone graft, and microthread presence were significantly correlated with implant fractures. Wide‐diameter implants had a reduced fracture risk within 90 months, after which the diameter did not correlate with fractures. Implants placed in the anterior mandible had a lower fracture risk within 90 months; mandibular premolar implants corresponded with a lower risk after 90 months. Implants without a history of bone graft or microthreads were more likely to fracture throughout the follow‐up time. Conclusions These results elucidate risk indicators for implant fractures and facilitate their reduction in clinical practice.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Implant fracture failure rate and potential associated risk indicators: An up to 12‐year retrospective study of implants in 5,124 patients

Kim

Clinical Oral Implants Res

et al. 2019

Self Cite

“…Dental implants have been used for more than half a century and currently are a highly reliable treatment option for long-term replacement (10+ years) of missing teeth [1,2]. As a long-term prognosis-established treatment, incidences of mechanical complications, such as fractures on fixtures or abutment screws, and biological complications, such as peri-implantitis, will inevitably occur [3][4][5][6]. An accurate understanding of the efficacy of different implant brands and the identification of treatment status are important for continued implant maintenance management and for dealing with any complications that arise.…”

Section: Introductionmentioning

confidence: 99%

Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images

et al. 2021

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy.

J Dent Res Dent Clin Dent Prospects

“…2 Recent studies have • Fernanda Pereira de Caxias 2 • Lisiane Cristina Bannwart 3 investigated the rates, pattern and potential risk of implant fractures. [6][7][8] Lee et al found lower risk of fractures in implants with wide-diameter microthreads, placed in patients with a history of bone graft, as well as those positioned in the mandibular anterior area. 6 Karl, Scherg and Grobecker-Karl reported that torque might be a risk factor for fracture in zirconia implants.…”

Section: Introductionmentioning

confidence: 99%

“…[6][7][8] Lee et al found lower risk of fractures in implants with wide-diameter microthreads, placed in patients with a history of bone graft, as well as those positioned in the mandibular anterior area. 6 Karl, Scherg and Grobecker-Karl reported that torque might be a risk factor for fracture in zirconia implants. 8 However, the literature reports a low incidence of implant fractures, but when it occurs, it can cause difficult problems for patients and dentists.…”

Section: Introductionmentioning

confidence: 99%

Influence of length, diameter and position of the implant in its fracture incidence: A Systematic Review

Goiato

Andreotti

Santos³

et al. 2019

Background. Implant fractures can cause difficult problems for patients and dentists. This systematic review aimed to determine the influence of some implant parameters on the occurrence of their fracture and to determine the incidence of fractures reported in recent years. Methods. A search was conducted in Pubmed database, from which 12 studies published in the last 12 years were selected. Results. This review reported a 2% incidence of implant fracture. Most implants had been in function between 3 and 4 years until fracture. The studies did not provide necessary information to establish a relationship between the different parameters of implants and the incidence of fractures. Conclusion. Thus, the indication of type, diameter and length of an implant and the bone quality in the region receiving it should be studied and accurately examined for each individual case in order to avoid future failures.