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

Prevalence of odontogenic cysts in oral and maxillofacial surgery department of hasan sadikin general hospital: 2 years retrospective study

Background: Odontogenic cysts are cysts arising from the odontogenic epithelium. According to the World Health Organization in 2017, classification of odontogenic cysts are classified into cysts originating from inflammatory and developmental processes. Most cystic lesions of the oral and maxillofacial cavity are of odontogenic origin and have higher incidence than other body parts. This study to explain about prevalence of odontogenic cyst in RSUP Hasan Sadikin in 2019-2020 .Method: This study was a retrospective study that included 30 patients diagnosed with odontogenic cysts during 2019-2020. The data taken in each patient were age, gender, location of predilection, investigations, diagnosis, management, and recurrence of cases of odontogenic cysts.Result: A total of 15 patients had developmental odontogenic cysts and 15 had inflammatory odontogenic cysts. Most developmental cysts were 12 patients with dentigerous cysts, Odontogenci Keratocyst in 2 patients, and calcifying odontogenic cyst in 1 case. Inflammatory cysts were dominated by 15 patients with radicular cysts.Conclusion: Prevalence of odontogenic cyst was found more in female, with radicular cyst and location in the maxill

Prevalence of odontogenic cysts in oral and maxillofacial surgery department of hasan sadikin general hospital: 2 years retrospective study

Background: Odontogenic cysts are cysts arising from the odontogenic epithelium. According to the World Health Organization in 2017, classification of odontogenic cysts are classified into cysts originating from inflammatory and developmental processes. Most cystic lesions of the oral and maxillofacial cavity are of odontogenic origin and have higher incidence than other body parts. This study to explain about prevalence of odontogenic cyst in RSUP Hasan Sadikin in 2019-2020 .Method: This study was a retrospective study that included 30 patients diagnosed with odontogenic cysts during 2019-2020. The data taken in each patient were age, gender, location of predilection, investigations, diagnosis, management, and recurrence of cases of odontogenic cysts.Result: A total of 15 patients had developmental odontogenic cysts and 15 had inflammatory odontogenic cysts. Most developmental cysts were 12 patients with dentigerous cysts, Odontogenci Keratocyst in 2 patients, and calcifying odontogenic cyst in 1 case. Inflammatory cysts were dominated by 15 patients with radicular cysts.Conclusion: Prevalence of odontogenic cyst was found more in female, with radicular cyst and location in the maxill

A comparative immunohistochemical study of Ki-67 expression in adenomatoid odontogenic tumour, unicystic ameloblastoma and dentigerous cyst

Magister Chirurgiae Dentium - MChDThe aim of this study was to investigate the biological profile oftheAOT by comparing the Ki-67 proliferative indices of the AOT, Unicystic Ameloblastoma (UA) and Dentigerous Cyst (DC) using ImmunoRatio® software. Adenomatoid Odontogenic Tumours (AOTs) are classified as benign epithelial odontogenic neoplasms with mature fibrous stroma, without odontogenic mesenchyme. However these lesions, like odontomes, occur almost exclusively during the final period of odontogenesis, and clinically behave like self-limiting hamartomatous lesions. Histologically they seem to arise from the lining of dental follicle

파노라마방사선영상에서 딥러닝 신경망을 이용한 치성 낭과 종양의 자동 진단 방법

학위논문 (박사) -- 서울대학교 대학원 : 치의학대학원 치의학과, 2021. 2. 이원진.Objective: The purpose of this study was to automatically diagnose odontogenic cysts and tumors of the jaw on panoramic radiographs using a deep convolutional neural network. A novel framework method of deep convolutional neural network was proposed with data augmentation for detection and classification of the multiple diseases. Methods: A deep convolutional neural network modified from YOLOv3 was developed for detecting and classifying odontogenic cysts and tumors of the jaw. Our dataset of 1,282 panoramic radiographs comprised 350 dentigerous cysts, 302 periapical cysts, 300 odontogenic keratocysts, 230 ameloblastomas, and 100 normal jaw with no disease. In addition, the number of radiographs was augmented 12-fold by flip, rotation, and intensity changes. The Intersection over union threshold value of 0.5 was used to obtain performance for detection and classification. The classification performance of the developed convolutional neural network was evaluated by calculating sensitivity, specificity, accuracy, and AUC (Area under the ROC curve) for diseases of the jaw. Results: The overall classification performance for the diseases improved from 78.2% sensitivity, 93.9% specificity, 91.3% accuracy, and 0.86 AUC using the convolutional neural network with unaugmented dataset to 88.9% sensitivity, 97.2% specificity, 95.6% accuracy, and 0.94 AUC using the convolutional neural network with augmented dataset. Convolutional neural network using augmented dataset had the following sensitivities, specificities, accuracies, and AUC: 91.4%, 99.2%, 97.8%, and 0.96 for dentigerous cysts, 82.8%, 99.2%, 96.2%, and 0.92 for periapical cysts, 98.4%, 92.3%, 94.0%, and 0.97 for odontogenic keratocysts, 71.7%, 100%, 94.3%, and 0.86 for ameloblastomas, and 100.0%, 95.1%, 96.0%, and 0.94 for normal jaw, respectively. Conclusion: The novel framework convolutional neural network method was developed for automatically diagnosing odontogenic cysts and tumors of the jaw on panoramic radiographs using data augmentation. The proposed convolutional neural network model showed high sensitivity, specificity, accuracy, and AUC despite the limited number of panoramic images involved.1. 목 적 구강악안면영역에서 발생하는 낭종 혹은 종양을 조기에 발견하지 못하여 적절한 치료가 이루어지지 못하고 지연되는 경우가 있다. 이러한 문제를 해결하기 위하여 인공신경망을 기반으로 하는 기계학습 기술인 딥러닝신경망(deep convolutional neural network)을 이용하는 컴퓨터 보조진단은 보다 정확하고 빠른 결과를 제공할 수 있다. 따라서 본 연구에서는 파노라마방사선영상에서 딥러닝신경망을 이용하여 구강악안면에서 자주 나타나는 4가지 질환(함치성낭, 치근단당, 치성각화낭, 법랑모세포종)을 자동으로 검출 및 진단하는 딥러닝신경망을 개발하고 그 정확성을 평가하였다. 2. 방 법 본 연구에서는 파노라마방사선영상에서 악골에 발생한 치성 낭과 종양을 검출하고 진단하기 위하여 YoLoV3를 기반으로 한 딥러닝신경망을 구축하였다. 1999년부터 2017년까지 서울대학교치과병원에서 조직병리학적으로 확진된 함치성낭 350례, 치근단낭 302례, 치성각화낭 300례, 법랑모세포종 230례의 환자로부터 획득한 총 1182매 파노라마방사선영상을 분석하였다. 또한 대조군으로 질환이 없는 정상 파노라마방사선영상 100매를 선택하였다. 파노라마방사선영상 데이터는 감마, 보정, 회전, 뒤집기 기법을 통하여 12배 증강되었다. 총 데이터의 60%는 훈련세트, 20%는 검증세트, 20%는 테스트세트로 사용하였다. 개발된 딥러닝신경망은 5배 교차검증(5-fold cross validation)기법을 이용하여 평가하였다. 본 연구에서 개발한 딥러닝신경망의 성능은 정확도(Accuracy), 민감도(sensitivity), 특이도(specificity) 및 ROC분석을 통한 AUC(area under the curve) 지표를 사용하여 측정하였다. 3. 결 과 본 연구에서 개발한 딥러닝신경망은 데이터 증강을 하지 않았을 때 78.2% 민감도, 93.9% 특이도, 91.3% 정확도 및 0.86의 AUC 값을 보였고 데이터 증강을 하였을 때에는 88.9% 민감도, 97.2% 특이도, 95.6% 정확도 및 0.94 AUC의 개선된 성능을 보여주었다. 함치성낭은 91.4% 민감도, 99.2% 특이도, 97.8% 정확도 및 0.96 AUC 값을 보였다. 치근단낭은 82.8% 민감도, 99.2% 특이도, 96.2% 정확도 및 0.92 AUC 값을 나타냈다. 치성각화낭은 98.4% 민감도, 92.3% 특이도, 94.0% 정확도 및 0.97 AUC 결과를 보였다. 법랑모세포종은 71.7% 민감도, 100% 특이도, 94.3% 정확도 및 0.86 AUC의 결과를 보였다. 그리고 정상적인 악골에서는 100% 민감도, 95.1% 특이도, 96.0% 정확도 및 0.97 AUC값을 각각 보였다. 4. 결 론 본 연구에서는 파노라마방사선영상에서 치성 낭과 종양을 자동으로 검출하고 진단하는 딥러닝신경망을 개발하였다. 본 연구는 파노라마방사선영상의 수가 충분하지 않았음에도 불구하고 데이터 증강 기법을 이용하여 우수한 민감도, 특이도 및 정확도 결과를 보였다. 본 연구결과를 통하여 개발된 시스템은 환자의 상기 질환을 조기에 진단하고 적절한 시기에 치료하는데 유용하다.Contents Abstract i Tables v Figure legends vi Introduction １ Materials and Methods ５ Data preparation and augmentation of panoramic radiographs ５ A deep convolutional neural network model for detection and classification of multiple diseases YOLOv3 ９ Evaluation of detection and classification performance of the deep convolutional neural network model １３ Results １５ Discussion ２８ Conclusion ３７ Acknowledgments ３８ References ３９ 요약(국문초록) ４８Docto

"Does human papilloma virus play a role in the histogenesis of the orthokeratinised jaw cyst?"

A research report submitted to the Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Dentistry Johannesburg, 2015Objectives: To analyse the clinico-pathological features of orthokeratinised jaw cysts (OJCs) and to determine whether human papillomavirus (HPV) DNA can be detected in OJCs. Material and methods: The clinical and radiological information of 30 patients diagnosed with OJCs were reviewed and the respective histology samples were studied for light microscopic features characteristic of HPV infection. The 30 OJCs were further evaluated for the presence of HPV by using consensus HPV polymerase chain reaction (PCR). Results: Patients with OJC ranged from 13 to 71-years (mean, 30.9 years; ± 12.9 years). There was a predilection for males (21/30). Most OJCs were found in the mandible (80%) and 44.8% were associated with an impacted tooth. Koilocyte-like characteristics were identified in 70% of cases, while 43.3% of cases showed a verruciform pattern of hyperkeratosis. All 30 OJCs were negative for HPV-DNA. Conclusion: HPV infection does not appear to play a role in the OJC and is not responsible for the wart-like histological changes that may be encountered in OJCs

A retrospective histopathologic review of paediatric oral and maxillofacial cases presented in Johannesburg, 1987-2007

MSc, Dentistry, Faculty of Health Sciences, University of the WitwatersrandThe characterisation of oral and maxillofacial histopathology found in children has been reported from developed countries of the west and in some developing countries in Africa but as yet not from South Africa. A retrospective study was designed to evaluate the epidemiological features of paediatric oral and maxillofacial histopathology seen at the University of the Witwatersrand’s Division of Oral Pathology from January 1987 to December 2007. A total of 1,258 children ≤ 16 years of age with histologically confirmed disease in the oral and/or maxillofacial region were recorded, with a male to female ratio of 1:1,05. A progressive increase in the frequency of oral and maxillofacial lesions was seen with increase in the age of the patient. Most lesions were concentrated in the 13-16 year age group (41,5%). Pathology involving the jaw bones formed the largest category of all oral and maxillofacial pathologies (40% of the total number of cases) and was predominated by odontogenic cysts and tumours (61,8%). Odontogenic tumours showed a significantly higher frequency in children over 12-years of age (P=0,006). A higher frequency of unicystic ameloblastoma than in the literature was noted. The remaining pathology, in decreasing order of frequency, involved the oral and perioral soft tissues (31,6%), the salivary glands (18%), oral mucosa (8,9%) and dental hard tissues (1,7%). Most lesions of soft tissue and salivary gland were reactive / inflammatory in nature and were outweighed by fibro-epithelial polyps and extravasation mucoceles respectively. Nearly two-thirds of the oral mucosal lesions were benign Human Papilloma Virus-induced lesions. Malignant neoplasms comprised 4,1% of the total number of cases with Burkitt’s lymphoma emerging as the most common malignancy. Although the smallest number of biopsy specimens was obtained from children younger than 5-years of age, the likelihood of a malignant diagnosis in the latter age group was substantially higher than in older children

Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs

Patients with odontogenic cysts and tumors may have to undergo serious surgery unless the lesion is properly detected at the early stage. The purpose of this study is to evaluate the diagnostic performance of the real-time object detecting deep convolutional neural network You Only Look Once (YOLO) v2-a deep learning algorithm that can both detect and classify an object at the same time-on panoramic radiographs. In this study, 1602 lesions on panoramic radiographs taken from 2010 to 2019 at Yonsei University Dental Hospital were selected as a database. Images were classified and labeled into four categories: dentigerous cysts, odontogenic keratocyst, ameloblastoma, and no cyst. Comparative analysis among three groups (YOLO, oral and maxillofacial surgeons, and general practitioners) was done in terms of precision, recall, accuracy, and F1 score. While YOLO ranked highest among the three groups (precision = 0.707, recall = 0.680), the performance differences between the machine and clinicians were statistically insignificant. The results of this study indicate the usefulness of auto-detecting convolutional networks in certain pathology detection and thus morbidity prevention in the field of oral and maxillofacial surgery.ope