Accuracy of computer-assisted image analysis in the diagnosis of maxillofacial radiolucent lesions: A systematic review and meta-analysis

Abstract

Objectives: This study aimed to search for scientific evidence concerning the accuracy of computer-assisted analysis for diagnosing maxillofacial radiolucent lesions. Methods: A systematic review was conducted according to the statements of Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols and considering 10 databases, including the gray literature. Protocol was registered at the International Prospective Register of Systematic Reviews (CRD42018089945). The population, intervention, comparison and outcome strategy was used to define the eligibility criteria and only diagnostic test studies were included. Their risk of bias was assessed by the Joanna Briggs Institute Critical Appraisal tool. Random-effects model meta-analysis was performed and heterogeneity among the included studies was estimated using the I2 statistic. The grade of recommendation, assessment, development, and evaluation (GRADE) tool assessed the quality of evidence and strength of recommendation across included studies. Results: Out of 715 identified citations, four papers, published between 2009 and 2017, fulfilled the criteria and were included in this systematic review. A total of 191 lesions, classified as periapical granuloma and cyst, dentigerous cyst or keratocystic odontogenic tumor, were analyzed. All selected articles scored low risk of bias. The pooled accuracy estimation, regardless of the classification method used, was 88.75% (95% CI = 85.19-92.30). Heterogeneity test reached moderate values (I2 = 57.89%). According to the GRADE tool, the analyzed outcome was classified as having low level of certainty. Conclusions: The overall evaluation showed all studies presented high accuracy rates of computer-aided diagnosis systems in classifying radiolucent maxillofacial lesions compared to histopathological biopsy. However, due to the moderate heterogeneity found among the studies included in this meta-analysis, a pragmatic recommendation about the use of computer-assisted analysis is not possible.

Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis

“…The reliability of computer-aided diagnosis systems has been studied recently in the diagnosis of maxillofacial radiolucent lesions and concluded that the current published evidence supports the accuracy of these methodologies in classifying the lesions compared to histopathological biopsy [ 26 ]. In this review, two interesting researches [ 14 , 27 ] discussed combined graph-based random walks segmentation with machine learning-based boosted classifiers to diagnose periapical cyst and periapical granuloma.…”

Periapical Lesions in Panoramic Radiography and CBCT Imaging—Assessment of AI’s Diagnostic Accuracy

“…To date, few meta-analyses and systematic reviews have been conducted on the utilization of AI for PL detection [58][59][60][61][62][63][64]. An important study by Silva et al [60] showed that the pooled diagnostic accuracy of CBCT for PL detection was 88.75% (95% confidence interval = 85. 19-92.30).…”

Accuracy of computer-aided image analysis in the diagnosis of odontogenic cysts: A systematic review

“…In head and neck areas, only few published articles exist that address computer-aided detection and classification of lesions related to dental structures ( 8 , 17 , 30 ). A recent systematic review and meta-analysis evaluated the accuracy of the novel approaches and demonstrated the effectiveness of CAD systems in classifying lesions based on cone beam computed tomography images ( 31 ). When digital analysis of odontogenic cysts is performed using microscopy images, especially for discriminating odontogenic keratocysts and radicular cysts, a variation of 86% to 100% was observed between the least and most accurate classification methods of the eligible studies, indicating a very promising future for an automated cyst class prediction.…”