Intraoral radiographs texture analysis for dental implant planning

Mundim, Mayara Barbosa Viandelli; Dias, Danilo Rocha; Costa, Ronaldo Martins da; Leles, Cláudio Rodrigues; Azevedo-Marques, Paulo Mazzoncini de; Ribeiro-Rotta, Rejane Faria

doi:10.1016/j.cmpb.2016.08.012

Cited by 19 publications

(20 citation statements)

References 22 publications

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“…This study showed that bone density changes in periapical radiographs could be objectively measured as an increase of mean gray levels and a decrease of CV. In radiographic images, an increase in bone trabeculae should increase brightness, while the SD and the CV should decrease . Models tested in this sample suggest that clinical factors such as time of functional loading, maximum bite force, and bruxism increase the bone density around IFCD distal implants.…”

Section: Discussionmentioning

confidence: 99%

“…A possible explanation may be the small size of ROIs used in the present investigation, which was determined by technical measurement reasons after a pilot study that indicated the best size of ROIs to obtain as much sample as possible. Mundim et al found a relation between some texture parameters with bone types and primary stability using larger ROIs in the apical area and on each side of the implant (mesial and distal). A finite elements study by Yoon et al showed that most stress transmitted to the peri‐implant bone is concentrated in the upper region of the implant body in contact with bone.…”

Section: Discussionmentioning

confidence: 99%

“…Radiographic changes of trabecular bone microstructure have been evaluated by quantitative methods to measure the variation of gray levels . Gray levels are generally assessed by means of first‐order statistics (mean, SD, and coefficient of variation [CV]), which describe the occurrence of gray levels without considering the spatial relation between pixels.…”

Section: Introductionmentioning

confidence: 99%

“…Gray levels are generally assessed by means of first‐order statistics (mean, SD, and coefficient of variation [CV]), which describe the occurrence of gray levels without considering the spatial relation between pixels. Second‐order statistics, which describe the properties of two related pixels in specific locations, can be used to analyze gray levels in medical imaging . Second‐order statistics are also called texture parameters, such as contrast, angular second moment, entropy, and correlation, which represent measures of uniformity, density, sharpness, regularity, and intensity .…”

Section: Introductionmentioning

confidence: 99%

“…[7][8][9] Radiographic changes of trabecular bone microstructure have been evaluated by quantitative methods to measure the variation of gray levels. [10][11][12][13][14] Gray levels are generally assessed by means of first-order statistics (mean, SD, and coefficient of variation [CV]), which describe the occurrence of gray levels without considering the spatial relation between pixels. Second-order statistics, which describe the properties of two related pixels in specific locations, can be used to analyze gray levels in medical imaging.…”

Section: Introductionmentioning

confidence: 99%

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Radiographic changes of trabecular bone density after loading of implant‐supported complete dentures: A 3‐year prospective study

Gerhardt

Villarinho

Rockenbach

et al. 2019

Clin Implant Dent Rel Res

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Background Bone tissues may undergo remodeling under functional mechanical stimuli. Purpose This prospective study on implant‐supported fixed complete dentures (IFCDs) evaluated the radiographic trabecular bone changes in density by means of gray levels and texture analysis variables after up to 3‐year loading. Materials and Methods The sample consisted of digital periapical radiographs of 63 distal implants of hybrid IFCDs installed in 30 patients (22 women, mean age of 62 ± 7.8 years). Digital periapical radiographs were taken after prosthesis installation, and 1 and 3 years after IFCD loading. Longitudinal images of each implant were superimposed, and the same regions of interest were selected for measurement of gray levels statistics (mean gray levels, SD, and coefficient of variation [CV]) and texture parameters (correlation, contrast, entropy, and angular second moment). Data were analyzed by mixed regression models. Results Mean gray levels increased for 1 year (P < .05), for 3 years (P < .01) and for maximum bite force (P < .01). The interaction between bruxism and time in 1 year was significant (P < .01) for a decrease in CV. No significant effect of texture analysis variables was found (P > .05). Conclusions The results suggest an increase of radiographic bone density as measured by an increase in mean gray levels and a decrease in CV in IFCD distal implants up to 3 years of loading.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Radiographic changes of trabecular bone density after loading of implant‐supported complete dentures: A 3‐year prospective study

Gerhardt

Villarinho

Rockenbach

et al. 2019

Clin Implant Dent Rel Res

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The CNN model aided the study of the clinical value hidden in the implant images

Huang,

Chen,

Zhong

et al. 2023

J Applied Clin Med Phys

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PurposeThis article aims to construct a new method to evaluate radiographic image identification results based on artificial intelligence, which can complement the limited vision of researchers when studying the effect of various factors on clinical implantation outcomes.MethodsWe constructed a convolutional neural network (CNN) model using the clinical implant radiographic images. Moreover, we used gradient‐weighted class activation mapping (Grad‐CAM) to obtain thermal maps to present identification differences before performing statistical analyses. Subsequently, to verify whether these differences presented by the Grad‐CAM algorithm would be of value to clinical practices, we measured the bone thickness around the identified sites. Finally, we analyzed the influence of the implant type on the implantation according to the measurement results.ResultsThe thermal maps showed that the sites with significant differences between Straumann BL and Bicon implants as identified by the CNN model were mainly the thread and neck area. (2) The heights of the mesial, distal, buccal, and lingual bone of the Bicon implant post‐op were greater than those of Straumann BL (P < 0.05). (3) Between the first and second stages of surgery, the amount of bone thickness variation at the buccal and lingual sides of the Bicon implant platform was greater than that of the Straumann BL implant (P < 0.05).ConclusionAccording to the results of this study, we found that the identified‐neck‐area of the Bicon implant was placed deeper than the Straumann BL implant, and there was more bone resorption on the buccal and lingual sides at the Bicon implant platform between the first and second stages of surgery. In summary, this study proves that using the CNN classification model can identify differences that complement our limited vision.

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Texture analysis of cone‐beam computed tomography images assists the detection of furcal lesion

Gonçalves

Araújo

Nussi

et al. 2020

Journal of Periodontology

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Background The aim of this study was to apply texture analysis (TA) to cone‐beam computed tomography (CBCT) scans of patients with grade C periodontitis for detection of non‐visible changes in the image. Methods TA was performed on CBCT scans of 34 patients with grade C periodontitis. Axial sections of CBCT were divided into three groups as follows: Group L (lesion) in which there is a furcal lesion with periodontal bone loss; Group I (intermediate) in which the border of the furcal lesion has normal characteristics; and Group C (control) in which the area is healthy. Eleven texture parameters were extracted from the region of interest. Mann‐Whitney U test was used to assess the differences in the texture between the three groups as follows: L versus I; L versus C, and I versus C. Results Statistically significant differences (P <0.05) were observed in almost all parameters in the intergroup analyses (i.e., L versus I and L versus C). However, statistical differences were smaller in groups I versus C in which only entropy of sum, entropy of difference, mean of sum, and variance of difference were statistically different (P < 0.05). Conclusion TA can potentially provide prognostic information to improve the diagnostic accuracy in the grading of the tissue around the furcal lesion, thus potentially accelerating the treatment decision‐making process.

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Intraoral radiographs texture analysis for dental implant planning

Cited by 19 publications

References 22 publications

Radiographic changes of trabecular bone density after loading of implant‐supported complete dentures: A 3‐year prospective study

Radiographic changes of trabecular bone density after loading of implant‐supported complete dentures: A 3‐year prospective study

The CNN model aided the study of the clinical value hidden in the implant images

Texture analysis of cone‐beam computed tomography images assists the detection of furcal lesion

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