Analysis of mandible trabecular structure using digital periapical radiographs to assess low bone quality in postmenopausal women

Diba, Silviana Farrah; Gracea, Rellyca Sola; Shantiningsih, Rurie Ratna; Hidjah, Khasnur

doi:10.1016/j.sdentj.2021.07.003

Cited by 6 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent methods have overcome the lack of quality of radiographic images, including low contrast, significant noise, and color homogeneity in the regions of interest (ROI). Several image processing methods have been investigated to detect osteoporosis using dental radiographs, including periapical [ 8 , 9 ] and panoramic [ 2 ] radiographs, as well as CBCT [ 10 ]. Another method, finite element analysis combined with ML, is used to predict hip fracture in DEXA images [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…The multilayer perceptron with backpropagation has been used in a wide range of applications, including optical character recognition and medical image analysis. Since periapical radiographs are a useful tool for predicting osteoporosis [ 9 ], we are interested in exploring these ML algorithms (decision tree, naive Bayes, and multilayer perceptron) as a classifier in this study. We hypothesize that the proposed method of color histogram of pixel-based clustering segmentation combined with an ML classifier can improve the diagnostic performance of osteoporosis on digital periapical radiographs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

Widyaningrum

Sela

Pulungan

et al. 2023

International Journal of Dentistry

View full text Add to dashboard Cite

Osteoporosis leads to the loss of cortical thickness, a decrease in bone mineral density (BMD), deterioration in the size of trabeculae, and an increased risk of fractures. Changes in trabecular bone due to osteoporosis can be observed on periapical radiographs, which are widely used in dental practice. This study proposes an automatic trabecular bone segmentation method for detecting osteoporosis using a color histogram and machine learning (ML), based on 120 regions of interest (ROI) on periapical radiographs, and divided into 60 training and 42 testing datasets. The diagnosis of osteoporosis is based on BMD as evaluated by dual X-ray absorptiometry. The proposed method comprises five stages: the obtaining of ROI images, conversion to grayscale, color histogram segmentation, extraction of pixel distribution, and performance evaluation of the ML classifier. For trabecular bone segmentation, we compare K-means and Fuzzy C-means. The distribution of pixels obtained from the K-means and Fuzzy C-means segmentation was used to detect osteoporosis using three ML methods: decision tree, naive Bayes, and multilayer perceptron. The testing dataset was used to obtain the results in this study. Based on the performance evaluation of the K-means and Fuzzy C-means segmentation methods combined with 3 ML, the osteoporosis detection method with the best diagnostic performance was K-means segmentation combined with a multilayer perceptron classifier, with accuracy, specificity, and sensitivity of 90.48%, 90.90%, and 90.00%, respectively. The high accuracy of this study indicates that the proposed method provides a significant contribution to the detection of osteoporosis in the field of medical and dental image analysis.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

Widyaningrum

Sela

Pulungan

et al. 2023

International Journal of Dentistry

View full text Add to dashboard Cite

show abstract

Comparison of quantitative radiomorphometric predictors of healthy and MRONJ-affected bone using panoramic radiography and cone-beam CT

Aslan,

Onem,

Mert

et al. 2024

Dentomaxillofacial Radiology

View full text Add to dashboard Cite

Objectives To determine the most distinctive quantitative radiomorphometric parameter(s) for the detection of MRONJ-affected bone changes in panoramic radiography (PR) and cone-beam computed tomography (CBCT). Methods PR and sagittal CBCT slices of 24 MRONJ patients and 22 healthy controls were used for the measurements of mandibular cortical thickness (MCT), fractal dimension (FD), lacunarity, mean gray value (MGV), bone area fraction (BA/TA), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), trabecular number (Tb.N). MCT was measured in the mental foramen region. While FD and lacunarity were measured on mandibular trabecular and cortical regions of interest (ROIs), the remaining parameters were measured on trabecular ROIs. The independent samples t-test was used to compare the measurements between MRONJ and control group for both imaging modalities (p = 0.05). Results MCT was the only parameter that differentiated MRONJ-affected bone in both PR and CBCT (p < 0.05). None of the remaining parameters revealed any difference for MRONJ-affected bone in CBCT (p > 0.05). FD, lacunarity, MGV, BA/TA, and Tb.Sp could distinguish MRONJ-affected trabecular bone in PR (p < 0.05). The correspondent ROI for both imaging methods that was reliable for detecting MRONJ-affected bone was the trabecular bone distal to the mental foramen above the inferior alveolar canal (ROI-3). Conclusions MCT is a reliable parameter for the discrimination of MRONJ-affected bone in both PR and CBCT images. PR may be used to detect MRONJ-affected trabecular bone using FD, lacunarity, MGV, BA/TA, and Tb.Sp measurements as well.

show abstract

Cone Beam Computed Tomography Panoramic Mandibular Indices in the Screening of Postmenopausal Women with Low Bone Mass: Correlations with Bone Quantity and Quality

Poiană,

Burcea,

Pițuru

et al. 2024

Dentistry Journal

View full text Add to dashboard Cite

Objective. This study examined the potential use of computed tomography panoramic mandibular indices on cone beam CT (CBCT) for assessing bone density in postmenopausal women with low bone mass. Study design. The study enrolled 104 postmenopausal women who underwent dual-energy X-ray absorptiometry (DXA) using a DXA scanner and mental foramen region CBCT alongside the NewTom VGi EVO Cone Beam 3D system. We assessed the relationship between the following DXA parameters: lumbar, femoral neck, and total hip T score, bone mineral density (BMD), and lumbar trabecular bone score (TBS). The following panoramic mandibular indices were also considered: the computed tomography mandibular index superior (CTI(S)), computed tomography mandibular index inferior (CTI(I)), and computed tomography mental index (CTMI). Results. The study revealed moderate correlations between CBCT indices and BMD/TBS scores: CTMI showed the highest correlation with the femoral neck T-score (r = 0.551, p < 0.0001). TBS scores were also moderately correlated with CBCT indices: CTMI showed a moderate positive correlation with TBS (r = 0.431, p < 0.0001); CTI(S) had a similar moderate positive correlation with TBS (r = 0.421, p < 0.0001). AUC values ranged from 0.697 to 0.733 for osteoporosis versus the osteopenia/normal group and from 0.734 to 0.744 for low versus normal bone quality groups, p < 0.0001. The comparison of the values of the studied indices between low versus normal bone quality (quantified with TBS) groups showed high sensitivity but low specificity. Conclusions. CBCT-measured indices CTI(S), CTI(I), and CTMI are useful in assessing patients with low bone mass to improve, by specific treatment, the prognosis of dental implants.

show abstract

Analysis of mandible trabecular structure using digital periapical radiographs to assess low bone quality in postmenopausal women

Cited by 6 publications

References 21 publications

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

Comparison of quantitative radiomorphometric predictors of healthy and MRONJ-affected bone using panoramic radiography and cone-beam CT

Cone Beam Computed Tomography Panoramic Mandibular Indices in the Screening of Postmenopausal Women with Low Bone Mass: Correlations with Bone Quantity and Quality

Contact Info

Product

Resources

About