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

This study aimed to search for scientific evidence concerning the accuracy of computer-assisted analysis for diagnosing odontogenic cysts. A systematic review was conducted according to the PRISMA statements and considering eleven databases, including the grey literature. Protocol was registered in PROSPERO (CRD 42020189349). The PECO strategy was used to define the eligibility criteria and only studies involving diagnostic accuracy were included. Their risk of bias was investigated using the Joanna Briggs Institute Critical Appraisal tool. Out of 437 identified citations, five papers, published between 2006 and 2019, fulfilled the criteria and were included in this systematic review. A total of 5,264 images from 508 lesions, classified as radicular cyst, odontogenic keratocyst, lateral periodontal cyst, glandular odontogenic cyst, or dentigerous cyst, were analyzed. All selected articles scored low risk of bias. In three studies, the best performances were achieved when the two subtypes of odontogenic keratocysts (solitary or syndromic) were pooled together, the case-wise analysis showing a success rate of 100% for odontogenic keratocysts and radicular cysts, in one of them. In two studies, the dentigerous cyst was associated with the majority of misclassifications, and its omission from the dataset improved significantly the classification rates. The overall evaluation showed all studies presented high accuracy rates of computer-aided systems in classifying odontogenic cysts in digital images of histological tissue sections. However, due to the heterogeneity of the studies, a meta-analysis evaluating the outcomes of interest was not performed and a pragmatic recommendation about their use is not possible

Application of artificial intelligence in the dental field : A literature review

Purpose: The purpose of this study was to comprehensively review the literature regarding the application of artificial intelligence (AI) in the dental field, focusing on the evaluation criteria and architecture types. Study selection: Electronic databases (PubMed, Cochrane Library, Scopus) were searched. Full-text articles describing the clinical application of AI for the detection, diagnosis, and treatment of lesions and the AI method/architecture were included. Results: The primary search presented 422 studies from 1996 to 2019, and 58 studies were finally selected. Regarding the year of publication, the oldest study, which was reported in 1996, focused on “oral and maxillofacial surgery.” Machine-learning architectures were employed in the selected studies, while approximately half of them (29/58) employed neural networks. Regarding the evaluation criteria, eight studies compared the results obtained by AI with the diagnoses formulated by dentists, while several studies compared two or more architectures in terms of performance. The following parameters were employed for evaluating the AI performance: accuracy, sensitivity, specificity, mean absolute error, root mean squared error, and area under the receiver operating characteristic curve. Conclusion: Application of AI in the dental field has progressed; however, the criteria for evaluating the efficacy of AI have not been clarified. It is necessary to obtain better quality data for machine learning to achieve the effective diagnosis of lesions and suitable treatment planning

PROTOTYPE REGION OF INTEREST (ROI) CITRA WAJAH MANUSIA BERBASIS BINARY LARGE OBJECT (BLOB) ANALYSIS

Setiap manusia memiliki identitasnya masing-masing, dan tidak akan sama satu identitas seseorang dengan identitas lainnya. Biometrik suatu wajah tidak akan sama dengan wajah lainnya, oleh karena itu dirancang suatu prototype pengenalan identitas ciri wajah pada wilayah-wilayah tertentu atau Region of Interest (ROI). ROI yang digunakan merupakan biometrik-biometrik unik yang terdapat pada wajah. Untuk mendapatkan ROI, proses segmentasi yang digunakan diantaranya adalah: Morfologi, Flood fill Algorithm dan Tresholding. Kemudian dengan menggunakan BLOB Analysis jumlah area dan nilai piksel yang terdapat pada ROI yang telah tersegmentasi akan dijadikan sebagai ekstraksi ciri pengenalan yang kemudian akan teridentifikasi menggunakan pendekatan Euclidean distance. ROI yang diperoleh dari ekstraksi menggunakan BLOB analysis mencakup 6 sampai 9 area biometrik wajah seperti alis, mata, hidung, mulut dan telinga. Hasil performansi dari identifikasi kemiripan wajah menggunakan 3 data wajah dengan 5 data sampel berbeda pada masing-masing wajah adalah 33,3%

Morphological Classification Of Odontogenic Keratocysts Using Bouligand-minkowski Fractal Descriptors

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The Odontogenic keratocyst (OKC) is a cystic lesion of the jaws, which has high growth and recurrence rates compared to other cysts of the jaws (for instance, radicular cyst, which is the most common jaw cyst type). For this reason OKCs are considered by some to be benign neoplasms. There exist two sub-types of OKCs (sporadic and syndromic) and the ability to discriminate between these sub-types, as well as other jaw cysts, is an important task in terms of disease diagnosis and prognosis. With the development of digital pathology, computational algorithms have become central to addressing this type of problem. Considering that only basic feature-based methods have been investigated in this problem before, we propose to use a different approach (the Bouligand - Minkowski descriptors) to assess the success rates achieved on the classification of a database of histological images of the epithelial lining of these cysts. This does not require the level of abstraction necessary to extract histologically-relevant features and therefore has the potential of being more robust than previous approaches. The descriptors were obtained by mapping pixel intensities into a three dimensional cloud of points in discrete space and applying morphological dilations with spheres of increasing radii. The descriptors were computed from the volume of the dilated set and submitted to a machine learning algorithm to classify the samples into diagnostic groups. This approach was capable of discriminating between OKCs and radicular cysts in 98% of images (100% of cases) and between the two sub-types of OKCs in 68% of images (71% of cases). These results improve over previously reported classification rates reported elsewhere and stiggest that Bouligand Minkowski descriptors are useful features to be used in histopathological images of these cysts.81110Sao Paulo Research Foundation [2012/19143-3, 2013/22205-3, 14/08026-1]CNPq (National Council for Scientific and Technological Development, Brazil) [307797/2014-7, 484312/2013-8]Engineering and Physical Sciences Research Council (EPSRC), UK [EP/M023869/1]Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq