Tag: nevin manimala

J Dent. 2023 Jun 13:104582. doi: 10.1016/j.jdent.2023.104582. Online ahead of print.

ABSTRACT

OBJECTIVES: To investigate the efficiency and accuracy of a deep learning-based automatic segmentation method for zygomatic bones from cone-beam computed tomography (CBCT) images.

METHODS: One hundred thirty CBCT scans were included and randomly divided into three subsets (training, validation, and test) in a 6:2:2 ratio. A deep learning-based model was developed, and it included a classification network and a segmentation network, where an edge supervision module was added to increase the attention of the edges of zygomatic bones. Attention maps were generated by the Grad-CAM and Guided Grad-CAM algorithms to improve the interpretability of the model. The performance of the model was then compared with that of four dentists on 10 CBCT scans from the test dataset. A p value <.05 was considered statistically significant.

RESULTS: The accuracy of the classification network was 99.64%. The Dice coefficient (Dice) of the deep learning-based model for the test dataset was 92.34 ± 2.04%, the average surface distance (ASD) was 0.1 ± 0.15 mm, and the 95% Hausdorff distance (HD) was 0.98 ± 0.42 mm. The model required 17.03 seconds on average to segment zygomatic bones, whereas this task took 49.3 minutes for dentists to complete. The Dice score of the model for the 10 CBCT scans was 93.2 ± 1.3%, while that of the dentists was 90.37 ± 3.32%.

CONCLUSIONS: The proposed deep learning-based model could segment zygomatic bones with high accuracy and efficiency compared with those of dentists.

CLINICAL SIGNIFICANCE: The proposed automatic segmentation model for zygomatic bone could generate an accurate 3D model for the preoperative digital planning of zygoma reconstruction, orbital surgery, zygomatic implant surgery, and orthodontics.

PMID:37321334 | DOI:10.1016/j.jdent.2023.104582

Eur J Radiol. 2023 Jun 8;165:110922. doi: 10.1016/j.ejrad.2023.110922. Online ahead of print.

ABSTRACT

PURPOSE: Patients with intracranial artery occlusion have high rates of ischaemic events and recurrence. Early identification of patients with high-risk factors is therefore beneficial for prevention. Here we assessed the association between the intravascular enhancement sign (IVES) on high-resolution vessel wall imaging (HR-VWI) and acute ischaemic stroke (AIS) in a population with middle cerebral artery (MCA) occlusion.

METHOD: We retrospectively analysed the records of 106 patients with 111 MCA occlusions, including 60 with and 51 without AIS, who had undergone HR-VWI and computed tomography angiography (CTA) examinations from November 2016 to February 2023. Numbers of IVES vessels were counted and compared to the CTA findings. Statistical analyses of demographic and medical data were also performed.

RESULTS: Occurrence rates and numbers of IVES vessels were significantly higher in the AIS than the non-AIS group (P < 0.05), and most vessels were detected on CTA. Numbers of vessels positively correlated with AIS occurrence (rho = 0.664; P < 0.0001). A multivariable ordinal logistic regression model adjusted for age, degree of wall enhancement, hypertension, and heart status identified the number of IVES vessels as an independent predictor for AIS (odds ratio = 1.6; 95% CI, 1.3-1.9; P < 0.0001).

CONCLUSION: Number of IVES vessels is an independent risk factor for AIS events, and may represent poor cerebral blood flow status and collateral compensation level. It thus provides cerebral haemodynamic information for patients with MCA occlusion for clinical use.

PMID:37320882 | DOI:10.1016/j.ejrad.2023.110922

Eur J Radiol. 2023 Jun 5;165:110920. doi: 10.1016/j.ejrad.2023.110920. Online ahead of print.

ABSTRACT

PURPOSE: To explore the added value of combining microcalcifications or apparent diffusion coefficient (ADC) with the Kaiser score (KS) for diagnosing BI-RADS 4 lesions.

METHODS: This retrospective study included 194 consecutive patients with 201 histologically verified BI-RADS 4 lesions. Two radiologists assigned the KS value to each lesion. Adding microcalcifications, ADC, or both these criteria to the KS yielded KS1, KS2, and KS3, respectively. The potential of all four scores to avoid unnecessary biopsies was assessed using the sensitivity and specificity. Diagnostic performance was evaluated by the area under the curve (AUC) and compared between KS and KS1.

RESULTS: The sensitivity of KS, KS1, KS2, and KS3 ranged from 77.1% to 100.0%.KS1 yielded significantly higher sensitivity than other methods (P < 0.05), except for KS3 (P > 0.05), most of all, when assessing NME lesions. For mass lesions, the sensitivity of these four scores was comparable (p > 0.05). The specificity of KS, KS1, KS2, and KS3 ranged from 56.0% to 69.4%, with no statistically significant differences(P > 0.05), except between KS1 and KS2 (p < 0.05).The AUC of KS1 (0.877) was significantly higher than that of KS (0.837; P = 0.0005), particularly for assessing NME (0.847 vs 0.713; P < 0.0001).

CONCLUSION: KS can stratify BI-RADS 4 lesions to avoid unnecessary biopsies. Adding microcalcifications, but not adding ADC, as an adjunct to KS improves diagnostic performance, particularly for NME lesions. ADC provides no additional diagnostic benefit to KS. Thus, only combining microcalcifications with KS is most conducive to clinical practice.

PMID:37320881 | DOI:10.1016/j.ejrad.2023.110920

Northwest Med Surg J. 1856 Jan;5(1):47-53.

NO ABSTRACT

PMID:37320580 | PMC:PMC9970425

Northwest Med Surg J. 1855 Mar;4(3):139-141.

NO ABSTRACT

PMID:37320302 | PMC:PMC9956402

Northwest Med Surg J. 1854 Mar;3(3):140-141.

NO ABSTRACT

PMID:37320266 | PMC:PMC9946641

Northwest Med Surg J. 1851 May;4(1):84-85.

NO ABSTRACT

PMID:37320133 | PMC:PMC9943205

Northwest Med Surg J. 1852 May;1(1):47.

NO ABSTRACT

PMID:37320019 | PMC:PMC9937401

Northwest Med Surg J. 1850 May;3(1):87.

NO ABSTRACT

PMID:37319754 | PMC:PMC9934192

Northwest Med Surg J. 1849 Apr-May;2(1):90.

NO ABSTRACT

PMID:37319675 | PMC:PMC9928371