Intelligent diagnostic model of keratoconus based on deep learning algorithm / 国际眼科杂志(Guoji Yanke Zazhi)

AIM: To establish an intelligent diagnostic model of keratoconus for small-diameter corneas by data mining and analysis of patients' clinical data.METHODS: Diagnostic study. A total of 830 patients(830 eyes)were collected, including 338 male(338 eyes)and 492 female(492 eyes), with an average age of 14-36(23.19±5.71)years. Among them, 731 patients(731 eyes)had undergone corneal refractive surgery at Chongqing Nanping Aier Eye Hospital from January 2020 to March 2022, and 99 patients had a diagnosed keratoconus from January 2015 to March 2022. Corneal diameter ≤11.1 mm was measured by Pentacam in all patients. Two cornea specialists classified patients' data into normal corneas, suspect keratoconus, and keratoconus groups based on the Belin/Ambrósio enhanced ectasia display(BAD)system in Pentacam. The data of 665 patients were randomly selected as the training set and the other 165 patients as the validation set by computer random sampling method. Seven parametric corneal features were extracted by convolutional neural networks(CNN), and the models were built by Residual Network(ResNet), Vision Transformer(ViT), and CNN+Transformer, respectively. The diagnostic accuracy of models was verified by cross-entropy loss and cross-validation method. In addition, sensitivity and specificity were evaluated using receiver operating characteristic curve.RESULTS: The accuracy of ResNet, ViT, and CNN+Transfermer for the diagnosis of normal cornea and suspect keratoconus was 85.57%, 86.11%, and 86.54% respectively, and the area under the receiver operating characteristic curve(AUC)was 0.823, 0.830 and 0.842 respectively. The accuracy of models for the diagnosis of suspect keratoconus and keratoconus was 97.22%, 95.83%, and 98.61%, respectively, and the AUC was 0.951, 0.939, and 0.988 respectively.CONCLUSION: For corneas ≤11.1 mm in diameter, the data model established by CNN+Transformer has a high accuracy rate for classifying keratoconus, which provides real and effective guidance for early screening.

Herramienta para la detección automática de nódulos pulmonares solitarios en series de imágenes de tomografía computarizada multicorte / A tool for automated detection of solitary pulmonary nodules in series of multicut computerized tomography images

Introducción: el nódulo pulmonar solitario es uno de los problemas más frecuentes en la práctica del radiólogo, que constituye un hallazgo incidental habitual en los estudios torácicos realizados durante el ejercicio clínico diario. Objetivo: implementar un sistema de diagnóstico asistido por computadora que facilite la detección del nódulo pulmonar solitario en las series de imágenes de tomografía computarizada multicorte. Métodos: se utilizó Matlab para el desarrollo y evaluación de un conjunto de algoritmos que constituyen elementos necesarios de un sistema de diagnóstico asistido por computadora. En orden: un algoritmo para la extracción de las regiones de interés, algoritmo para la extracción de características y un algoritmo de detección de nódulo pulmonar solitario para el cual se probaron varios clasificadores. La evaluación de los algoritmos fue efectuada en base a las anotaciones realizada por especialistas a la colección de imágenes LIDC-IDRI (Lung Image Database Consortium). Resultados: el método de segmentación empleado para extracción de las regiones de interés permitió generar la adecuada división de las imágenes originales en regiones significativas. El algoritmo utilizado en la detección mostró para el conjunto de prueba además de buena exactitud (de 96,4 por ciento), un buen balance de sensibilidad (91,5 por ciento) para una tasa de 0,84 falsos positivos por imagen. Conclusiones: el trabajo de investigación y la implementación realizada se reflejan en la construcción de una interfaz gráfica en Matlab como prototipo del sistema de diagnóstico asistido por computadora, con el que se puede contribuir a detectar más fácilmente el NPS(AU)

Introduction: solitary pulmonary nodules are one of the most frequent problems in radiographic practice. They are a common incidental finding in chest studies conducted during routine clinical work. Objective: implement a computer-assisted diagnostic system facilitating detection of solitary pulmonary nodules in multicut computerized tomography image series. Methods: Matlab was used to develop and evaluate a set of algorithms constituting necessary components of a computer-assisted diagnostic system. The order was the following: an algorithm to extract regions of interest, another to extract characteristics, and another to detect solitary pulmonary nodules, for which several classifiers were tested. Evaluation of the algorithms was based on notes taken by specialists on the LIDC-IDRI (Lung Image Database Consortium) image collection. Results: the segmentation method used for extraction of regions of interest made it possible to create a suitable division of the original images into significant regions. The algorithm used for detection found that the test set exhibited good accuracy (96.4%), a good sensitivity balance (91.5%), and a 0.84 rate of false positives per image. Conclusions: the research and implementation work done is reflected in the construction of a Matlab graphic interface serving as a prototype for a computer-assisted diagnostic system which may facilitate detection of SPNs.

Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging

Abstract Background: Currently, magnetic resonance imaging (MRI) is used to evaluate active inflammatory sacroiliitis related to axial spondyloarthritis (axSpA). The qualitative and semiquantitative diagnosis performed by expert radiologists and rheumatologists remains subject to significant intrapersonal and interpersonal variation. This encouraged us to use machine-learning methods for this task. Methods: In this retrospective study including 56 sacroiliac joint MRI exams, 24 patients had positive and 32 had negative findings for inflammatory sacroiliitis according to the ASAS group criteria. The dataset was randomly split with ∼ 80% (46 samples, 20 positive and 26 negative) as training and ∼ 20% as external test (10 samples, 4 positive and 6 negative). After manual segmentation of the images by a musculoskeletal radiologist, multiple features were extracted. The classifiers used were the Support Vector Machine, the Multilayer Perceptron (MLP), and the Instance-Based Algorithm, combined with the Relief and Wrapper methods for feature selection. Results: Based on 10-fold cross-validation using the training dataset, the MLP classifier obtained the best performance with sensitivity = 100%, specificity = 95.6% and accuracy = 84.7%, using 6 features selected by the Wrapper method. Using the test dataset (external validation) the same MLP classifier obtained sensitivity = 100%, specificity = 66.7% and accuracy = 80%. Conclusions: Our results show the potential of machine learning methods to identify SIJ subchondral bone marrow edema in axSpA patients and are promising to aid in the detection of active inflammatory sacroiliitis on MRI STIR sequences. Multilayer Perceptron (MLP) achieved the best results.(AU)

Construction of artificial intelligence assisted diagnosis model for colonoscopy / 中华消化内镜杂志

Objective To establish an artificial intelligence deep learning model using clinical colonoscopy images and video to assist the diagnosis by colonoscopy. Methods More than 600000 colonoscopy images were collected in endoscopic center of the Second Affiliated Hospital of Zhejiang University School of Medicine from 2014 to 2018, and endoscopic experts recorded a large number of high-quality operation video of colonoscopy as analysis data. After repeated discussion by six experts, the classified intestinal sites and pathological features were determined, and fuzzy and confusable images were deleted. The final selection result was approximately 1 out of 4. And then the features of images were marked using an independently developed software. The deep learning algorithm was developed using TensorFlow platform of Google. Results After repeated comparison and analysis of the results of machine training and judgment results combined with pathology from endoscopic experts, the sensitivity of the model for some diseases ( such as colon polyps) was 99％ under laboratory conditions. In the clinical colonoscopy test, the sensitivity, specificity, and overall accuracy of this model for diagnosis of colon polyps were 98. 30％(4187/4259), 88. 10％ (17620/20000), and 92. 92％ [2×98. 30％×88. 10％/(98. 30％+88. 10％)], respectively. The sensitivity and specificity for ulcerative colitis were 78. 32％ ( 2671/3410) , and 67. 06％(13412/20000), respectively. The diagnosis time spent on a single image was 0. 5±0. 03 s, and it was the real time for application, including system recognition, text prompt in video image, background record and storage. Conclusion The artificial intelligence assisted diagnosis model developed by our team can identify colonic polyps, colorectal cancer, colorectal eminence, colonic diverticulum, ulcerative colitis, etc. The auxiliary diagnosis model of colon disease can guide beginners to carry out colonoscopy, and can improve lesion detection rate, reduce misdiagnosis rate, and improve the overall operating efficiency of endoscopic center, which is conducive to the quality control of colonoscopy.

Application of artificial intelligence assisted diagnosis based on deep learning for early esophageal cancer / 中华消化内镜杂志

Objective To improve the detection rate of early esophageal cancer during endoscopy by construction of artificial intelligence assistant diagnosis system. Methods A total of 2400 esophageal images were collected from Zhongshan Hospital of Fudan University from January 2016 to December 2017, including 1200 images of early esophageal cancer and 1200 images of normal esophageal mucosa. The lesions in pictures were marked with rectangular box by using computer program. Among them, 2000 pictures were divided into the training set and 400 pictures into the test set. An assistant diagnostic model of early esophageal cancer was established by back propagation algorithm in computer deep learning. The training model was tested and the sensitivity and specificity of the system at different cut-off points in the test set was calculated. Receiver operating characteristic ( ROC) curve was used to evaluate the performance of the diagnostic model. Results The area under ROC curve ( AUC) of the auxiliary diagnostic model was 0. 9961. The sensitivity and specificity were satisfactory. Conclusion The deep learning model constructed in this study has good specificity, sensitivity and AUC value in the diagnosis of early esophageal cancer, and can assist endoscopists in real-time diagnosis in clinical examination.

Research status and prospect of deep learning algorithm-based artificial intelligence in assisted diagnosis of diabetic retinopathy / 中华实验眼科杂志

Based on deep learning algorithm, big stride development has been made about artificial intelligence ( AI) technology,both in its basic theory and clinical ophthalmic image analysis. AI can diagnose diabetic retinopathy ( DR) automatically by using color fundus photography. Compared with other ophthalmic diseases, DR assisted diagnosis with AI might be far more advanced technic. Benefited from advantage of fast diagnostic speed,high accuracy and accordingly saved human resources, great potential can be expected in AI-assisted DR screening and grading. However,as a recently developed interdisciplinary technology,deep learning-based AI-aided DR screening system still needs multidisciplinary cooperation and resources sharing to get further development,such as overcoming data standardization, real-world verification and productization issues. Although challenges coexist, AI applied in ophthalmology clinical practice can be realized with technical development and widespread concern of society.

Validation and application of an artificial intelligence robot assisted diagnosis system for diabetic retinopathy / 中华实验眼科杂志

Objective To evaluate the performance of an artificial intelligence ( AI ) assisted diagnosis system for diabetic retinopathy ( DR) based on deep learning theory. Methods Diagnostic performance of a robot assisted diagnosis system called SongYue for DR was trained by using 25297 retinal images tagged by fundus doctors from multiple hospitals in China. Four types of DR detection model consisting of abnormal DR,referable DR,severe non-proliferative and proliferative DR as well as proliferative DR according to fundus leisions identification were established. The ability of the system to distinguish DR was determined by using receiver operator characteristic (ROC) analysis,sensitivity and specificity of the system. Results SongYue system achieved an area under the ROC curve ( AUC) of 0. 920 for successfully distinguishing normal images from those DR with a sensitivity of 96. 0％at a specificity of 87. 9％. The AUC of SongYue for referable DR was 0. 925,sensitivity was 90. 4％,and specificity was 95. 2％. For severe non-proliferative and proliferative DR,AUC was 0. 845,sensitivity was 72. 7％,and specificity was 96. 2％. For proliferative DR, AUC was 0. 855, sensitivity was 73. 5％, and specificity was 97. 3％. Conclusions SongYue robot assisted diagnosis system has high AUC,sensitivity and specificity for identifying DR, showing good clinical applicable benefits.

Automated assisted clinical diagnosis of retinopathy of prematurity based on deep learning / 中华实验眼科杂志

Objective To evaluate the application value of an intelligent fundus assisted diagnosis system for detecting retinopathy of prematurity ( ROP) based on deep learning. Methods A total of 38895 fundus images for premature infants screening were collected from Renmin Hospital of Wuhan University Eye Center and were labeled by 10 licensed ophthalmologists. A deep learning network model was established to acquire automatic classification of disease stages and plus disease. The accuracy,sensitivity and specificity of the algorithm were calculated to evaluate the performance of the artificial intelligence system for ROP automatic diagnosis. This study protocol was approved by Ethic Committee of Renmin Hospital of Wuhan University ( No. WDRY2019-K032 ) . Written informed consent was obtained from the guardians of the children before entering the study cohort. Results The intelligent system achieved an accuracy of 0. 931. Specifically,the accuracies in detecting demarcation line (stageⅠ) was 0. 876,ridge (stage Ⅱ) was 0. 942,ridge with extra retinal fibrovascular (stageⅢ) was 0. 968,subtotal retinal detachment (stageⅣ) was 0. 998,total retinal detachment (stage Ⅴ) was 0. 999,vascular tortuosity and dilatation (plus disease) was 0. 896,optic disc was 0. 954,macular was 0. 781,and laser scars were 0. 974,respectively. Conclusions Deep learning algorithm can detect the stages and plus disease of ROP with excellent accuracy,and it provides the feasibility of applying the algorithm for ROP automated screening in clinical.

Confiabilidade da análise de Bolton e da discrepância de modelos realizadas em modelos digitais / Reliability of Bolton analysis and discrepancy of models performed in emodels

Modelos de estudo propiciam uma avaliação tridimensional dos arcos dentários superior e inferior, bem como da relação oclusal, sendo fundamentais ao diagnóstico e plano de tratamento de pacientes. Com o avanço da tecnologia e devido algumas desvantagens encontradas nos modelos tradicionais, a Ortodontia passou a empregar modelos digitais em que as análises antes realizadas com paquímetros são agora obtidas através de softwares. O presente estudo teve como objetivo avaliar a confiabilidade da análise de Bolton e da discrepância de modelos realizadas em modelos digitais. Quinze pares de modelos de gesso de pacientes foram selecionados e mesurados através de um paquímetro digital (Kingtools, São Paulo, Brasil, 150 mm, precisão 0,01 mm/.0005'). Posteriormente, os modelos foram escaneados pelo scanner Go Digital (Open Technologies, Itália). Para os respectivos modelos digitais obtidos, o software Maslab (Istituto di Scienza e Tecnologie de'll Informazione, Pisa, Itália) foi utilizado para medição. Todas as medidas e análises foram realizadas por um único examinador. Conduziu-se a análise estatística pelo software Graph Pad Prism 5.2 (GraphPad Software, Inc. Califórnia), e aplicou-se o teste de Mann Whitney, com intervalo de confiança de 95% e significância estabelecida em p<0,05.(AU)

Dental models provide a three-dimensional view of the upper and lower arches and the occlusal relationship, they are essential tools for the diagnosis and patient treatment plan. Due to technology advancement and some disadvantages found in traditional models, orthodontics started to use digital models (emodels) in which the analysis previously performed with calipers are now obtained through softwares. This study aimed to evaluate the reliability of Bolton analysis and the discrepancy of models performed in digital models. Fifteen pairs of patients plaster models were selected and measured with a digital caliper (Kingtools, São Paulo, Brazil, 150 mm, precision of 0,01 mm/.0005). The models were then scanned by Go Digital Scanner (Open Technologies, Italy). For the respective digital models obtained, Maslab software (Istituto di Scienza e Tecnologie de'll Informazione, Pisa, Italy) was used for measuring. All measurements and analyses were performed by a single examiner. The statistical analysis was conducted through the software Graph Pad Prism 5.2 (GraphPad Software, Inc. California) and the Mann-Whitney test was applied, with confidence interval of 95% and significance level of p<0,05. (AU)

Breast Cancer Detection in a Screening Population: Comparison of Digital Mammography, Computer-Aided Detection Applied to Digital Mammography and Breast Ultrasound / 한국유방암학회지

PURPOSE: We aimed to compare the detection of breast cancer using full-field digital mammography (FFDM), FFDM with computer-aided detection (FFDM+CAD), ultrasound (US), and FFDM+CAD plus US (FFDM+CAD+US), and to investigate the factors affecting cancer detection. METHODS: In this retrospective study conducted from 2008 to 2012, 48,251 women underwent FFDM and US for cancer screening. One hundred seventy-one breast cancers were detected: 115 invasive cancers and 56 carcinomas in situ. Two radiologists evaluated the imaging findings of FFDM, FFDM+CAD, and US, based on the Breast Imaging Reporting and Data System lexicon of the American College of Radiology by consensus. We reviewed the clinical and the pathological data to investigate factors affecting cancer detection. We statistically used generalized estimation equations with a logit link to compare the cancer detectability of different imaging modalities. To compare the various factors affecting detection versus nondetection, we used Wilcoxon rank sum, chi-square, or Fisher exact test. RESULTS: The detectability of breast cancer by US (96.5%) or FFDM+CAD+US (100%) was superior to that of FFDM (87.1%) (p=0.019 or p<0.001, respectively) or FFDM+ CAD (88.3%) (p=0.050 or p<0.001, respectively). However, cancer detectability was not significantly different between FFDM versus FFDM+CAD (p=1.000) and US alone versus FFDM+CAD+US (p=0.126). The tumor size influenced cancer detectability by all imaging modalities (p<0.050). In FFDM and FFDM+CAD, the nondetecting group consisted of younger patients and patients with a denser breast composition (p<0.050). In breast US, carcinoma in situ was more frequent in the nondetecting group (p=0.014). CONCLUSION: For breast cancer screening, breast US alone is satisfactory for all age groups, although FFDM+ CAD+US is the perfect screening method. Patient age, breast composition, and pathological tumor size and type may influence cancer detection during screening.

Automated Detection Algorithm of Breast Masses in Three-Dimensional Ultrasound Images / 대한의료정보학회지

OBJECTIVES: We propose an automatic breast mass detection algorithm in three-dimensional (3D) ultrasound (US) images using the Hough transform technique. METHODS: One hundred twenty-five cropped images containing 68 benign and 60 malignant masses are acquired with clinical diagnosis by an experienced radiologist. The 3D US images are masked, subsampled, contrast-adjusted, and median-filtered as preprocessing steps before the Hough transform is used. Thereafter, we perform 3D Hough transform to detect spherical hyperplanes in 3D US breast image volumes, generate Hough spheres, and sort them in the order of votes. In order to reduce the number of the false positives in the breast mass detection algorithm, the Hough sphere with a mean or grey level value of the centroid higher than the mean of the 3D US image is excluded, and the remaining Hough sphere is converted into a circumscribing parallelepiped cube as breast mass lesion candidates. Finally, we examine whether or not the generated Hough cubes were overlapping each other geometrically, and the resulting Hough cubes are suggested as detected breast mass candidates. RESULTS: An automatic breast mass detection algorithm is applied with mass detection sensitivity of 96.1% at 0.84 false positives per case, quite comparable to the results in previous research, and we note that in the case of malignant breast mass detection, every malignant mass is detected with false positives per case at a rate of 0.62. CONCLUSIONS: The breast mass detection efficiency of our algorithm is assessed by performing a ROC analysis.

Evaluation of Semi-automatic Segmentation Methods for Persistent Ground Glass Nodules on Thin-Section CT Scans / 대한의료정보학회지

OBJECTIVES: This work was a comparative study that aimed to find a proper method for accurately segmenting persistent ground glass nodules (GGN) in thin-section computed tomography (CT) images after detecting them. METHODS: To do this, we first applied five types of semi-automatic segmentation methods (i.e., level-set-based active contour model, localized region-based active contour model, seeded region growing, K-means clustering, and fuzzy C-means clustering) to preprocessed GGN images, respectively. Then, to measure the similarities, we calculated the Dice coefficient of the segmented area using each semiautomatic method with the result of the manually segmented area by two radiologists. RESULTS: Comparison experiments were performed using 40 persistent GGNs. In our experiment, the mean Dice coefficient for each semiautomatic segmentation tool with manually segmented area was 0.808 for the level-set-based active contour model, 0.8001 for the localized region-based active contour model, 0.629 for seeded region growing, 0.7953 for K-means clustering, and 0.7999 for fuzzy C-means clustering, respectively. CONCLUSIONS: The level-set-based active contour model algorithm showed the best performance, which was most similar to the result of manual segmentation by two radiologists. From the differentiation between the normal parenchyma and the nodule, it was also the most efficient. Effective segmentation methods will be essential for the development of computer-aided diagnosis systems for more accurate early diagnosis and prognosis of lung cancer in thin-section CT images.

Reliability of Bolton analysis evaluation in tridimensional virtual models

Objective: The present study aimed at evaluating the reliability of Bolton analysis in tridimensional virtual models, comparing it with the manual method carried out with dental casts.Methods:The present investigation was performed using 56 pairs of dental casts produced from the dental arches of patients in perfect conditions and randomly selected from Universidade Federal da Bahia, School of Dentistry, Orthodontics Postgraduate Program. Manual measurements were obtained with the aid of a digital Cen-Tech 4"(r) caliper (Harpor Freight Tools, Calabasas, CA, USA). Subsequently, samples were digitized on 3Shape(r)R-700T scanner (Copenhagen, Denmark) and digital measures were obtained by Ortho Analyzer software.Results:Data were subject to statistical analysis and results revealed that there were no statistically significant differences between measurements with p-values equal to p = 0.173 and p= 0.239 for total and anterior proportions, respectively.Conclusion:Based on these findings, it is possible to deduce that Bolton analysis performed on tridimensional virtual models is as reliable as measurements obtained from dental casts with satisfactory agreement.

Objetivo: o presente estudo teve como objetivo avaliar a confiabilidade da análise de Bolton em modelos virtuais tridimensionais, comparando-a com a realizada em modelos de gesso.Métodos:foram utilizados 56 pares de modelos de gesso das arcadas dentárias de pacientes oriundos do Curso de Especialização em Ortodontia da Faculdade de Odontologia da Universidade Federal da Bahia, escolhidos aleatoriamente e em perfeito estado. Medidas manuais foram obtidas utilizando-se o paquímetro digital Cen-Tech(r) 4" (Harpor Freight Tools, Calabasas, CA, EUA). Em seguida, os mesmos foram digitalizados pelo scanner R-700TM(3Shape(r), Copenhagen, Dinamarca) e, por meio do programa Ortho AnalyzerTM, da mesma marca, foram obtidas as medidas digitais.Resultados:os dados foram submetidos a testes estatísticos e os resultados demonstraram que não houve diferença estatisticamente significativa nos dois tipos de medições com valores de p = 0,173 e p = 0,239, respectivamente, para as proporções total e anterior.Conclusão:com base nesses achados, pode-se inferir que a análise de Bolton realizada em modelos virtuais tridimensionais é tão confiável quanto a obtida em modelos de gesso, apresentando uma concordância satisfatória.

Look what else we found - clinically significant abnormalities detected during routine ROP screening.

Purpose: The purpose of this study was to report the spectrum of anterior and posterior segment diagnoses in Asian Indian premature infants detected serendipitously during routine retinopathy of prematurity (ROP) screening during a 1 year period. Methods: A retrospective review of all Retcam (Clarity MSI, USA) imaging sessions during the year 2011 performed on infants born either <2001 g at birth and/ or <34.1 weeks of gestation recruited for ROP screening was performed. All infants had a minimum of seven images at each session, which included the dilated anterior segment, disc, and macula center and the four quadrants using the 130° lens. Results: Of the 8954 imaging sessions of 1450 new infants recruited in 2011, there were 111 (7.66%) with a diagnosis other than ROP. Anterior segment diagnoses seen in 31 (27.9%) cases included clinically significant cataract, lid abnormalities, anophthalmos, microphthalmos, and corneal diseases. Posterior segment diagnoses in 80 (72.1%) cases included retinal hemorrhages, cherry red spots, and neonatal uveitis of infective etiologies. Of the 111 cases, 15 (13.5%) underwent surgical procedures and 24 (21.6%) underwent medical procedures; importantly, two eyes with retinoblastoma were detected which were managed timely. Conclusions: This study emphasizes the importance of ocular digital imaging in premature infants. Visually significant, potentially life‑threatening, and even treatable conditions were detected serendipitously during routine ROP screening that may be missed or detected late otherwise. This pilot data may be used to advocate for a possible universal infant eye screening program using digital imaging.

Tooth-size discrepancy: A comparison between manual and digital methods

INTRODUCTION: Technological advances in Dentistry have emerged primarily in the area of diagnostic tools. One example is the 3D scanner, which can transform plaster models into three-dimensional digital models. OBJECTIVE: This study aimed to assess the reliability of tooth size-arch length discrepancy analysis measurements performed on three-dimensional digital models, and compare these measurements with those obtained from plaster models. MATERIAL AND METHODS: To this end, plaster models of lower dental arches and their corresponding three-dimensional digital models acquired with a 3Shape R700T scanner were used. All of them had lower permanent dentition. Four different tooth size-arch length discrepancy calculations were performed on each model, two of which by manual methods using calipers and brass wire, and two by digital methods using linear measurements and parabolas. RESULTS: Data were statistically assessed using Friedman test and no statistically significant differences were found between the two methods (P > 0.05), except for values found by the linear digital method which revealed a slight, non-significant statistical difference. CONCLUSIONS: Based on the results, it is reasonable to assert that any of these resources used by orthodontists to clinically assess tooth size-arch length discrepancy can be considered reliable. .

INTRODUÇÃO: na Odontologia, os avanços tecnológicos vêm se manifestando, principalmente, em instrumentos de diagnóstico, como o desenvolvimento dos scanners 3D, capazes de transformar modelos de gesso em modelos digitais tridimensionais. OBJETIVO: o objetivo da presente pesquisa foi avaliar a confiabilidade da análise da Discrepância de Modelo realizada em modelos digitais tridimensionais, comparando-a com a obtida em modelos de gesso. MÉTODOS: utilizou-se modelos de gesso das arcadas dentárias inferiores e seus correspondentes modelos digitais tridimensionais, adquiridos por meio do scanner 3Shape R700T. Foram realizados quatro diferentes cálculos da Discrepância de Modelo para cada modelo selecionado, dois desses por meio de métodos manuais, utilizando paquímetro e fio de latão, e dois por meio de métodos digitais, utilizando medições lineares e por meio da confecção de uma parábola. RESULTADOS: os dados obtidos foram avaliados estatisticamente por meio do teste de Friedman, e observou-se não haver diferença estatisticamente significativa entre os métodos utilizados (p > 0,05), exceto os valores obtidos pelo método digital linear, onde observou-se uma pequena diferença estatística, porém, não são valores considerados clinicamente significativos. CONCLUSÃO: com base nos resultados, é possível afirmar que, quaisquer desses recursos que o ortodontista venha a utilizar em sua vida clínica para obtenção da Discrepância de Modelo, esses são considerados métodos confiáveis. .

Fate of pulmonary nodules detected by computer-aided diagnosis and physician review on the computed tomography simulation images for hepatocellular carcinoma

PURPOSE: To investigate the frequency and clinical significance of detected incidental lung nodules found on computed tomography (CT) simulation images for hepatocellular carcinoma (HCC) using computer-aided diagnosis (CAD) and a physician review. MATERIALS AND METHODS: Sixty-seven treatment-naive HCC patients treated with transcatheter arterial chemoembolization and radiotherapy (RT) were included for the study. Portal phase of simulation CT images was used for CAD analysis and a physician review for lung nodule detection. For automated nodule detection, a commercially available CAD system was used. To assess the performance of lung nodule detection for lung metastasis, the sensitivity, negative predictive value (NPV), and positive predictive value (PPV) were calculated. RESULTS: Forty-six patients had incidental nodules detected by CAD with a total of 109 nodules. Only 20 (18.3%) nodules were considered to be significant nodules by a physician review. The number of significant nodules detected by both of CAD or a physician review was 24 in 9 patients. Lung metastases developed in 11 of 46 patients who had any type of nodule. The sensitivities were 58.3% and 100% based on patient number and on the number of nodules, respectively. The NPVs were 91.4% and 100%, respectively. And the PPVs were 77.8% and 91.7%, respectively. CONCLUSION: Incidental detection of metastatic nodules was not an uncommon event. From our study, CAD could be applied to CT simulation images allowing for an increase in detection of metastatic nodules.

New Parametric Imaging Method with Fluorescein Angiograms for Detecting Areas of Capillary Nonperfusion / 대한의료정보학회지

OBJECTIVES: Fluorescein angiography (FAG) is currently the most useful diagnostic modality for examining retinal circulation, and it is frequently used for the evaluation of patients with diabetic retinopathy, occlusive diseases, such as retinal venous and arterial occlusions, and wet macular degeneration. This paper presents a method for objectively evaluating retinal circulation by quantifying circulation-related parameters. METHODS: This method allows the semiautomatic preprocessing and registering of FAG images. The arterial input function is estimated from the registered set of FAG images using gamma-variate fitting. Then, the parameters can be computed by deconvolution on the basis of truncated singular value decomposition, and they can finally be presented as parametric color images in a combination of three colors, red, green, and blue. RESULTS: After the estimation of arterial input function, the parameters of relative blood flow and mean transit time were computed using deconvolution analysis based on truncated singular value decomposition. CONCLUSIONS: The parametric color image is helpful to interpret the status of retinal blood circulation and provides quantitative data on retina ischemia without interobserver variability. This system easily provides the status of retinal blood circulation both qualitatively and quantitatively. It also helps to standardize FAG interpretation and may contribute to network-based telemedicine systems in the future.

The evolution of cephalometric diagnosis in Orthodontics

INTRODUCTION: Although the development of CT have represented a landmark in diagnostic imaging, its use in Dentistry turned out very discretely over the years. With the appearance of programs for analysis of three-dimensional images, specific for Orthodontics and Orthognathic surgery, a new reality is being built. OBJECTIVE: The authors of this study aim to inform the orthodontic society of fundamentals about digital cephalometric radiographic image and computed tomography, discussing about: Field of view (FOV), radiation doses, demands for the use in Orthodontics and radiographic simulations.

INTRODUÇÃO: apesar do desenvolvimento da tomografia computadorizada ter representado um marco na área do diagnóstico por imagem, sua utilização em Odontologia deu-se de forma muito discreta ao longo dos anos. Com o surgimento de programas para análises de imagens tridimensionais, específicos para Ortodontia e Cirurgia Ortognática, uma nova realidade está sendo construída. OBJETIVO: os autores do presente artigo têm o objetivo de informar à sociedade ortodôntica fundamentos sobre imagem radiográfica cefalométrica digital e tomografia computadorizada, discutindo sobre o campo de visão (FOV), doses de radiação, exigências para o uso em Ortodontia e simulações radiográficas.

The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis

PURPOSE: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. MATERIALS AND METHODS: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. RESULTS: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. CONCLUSION: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

Detection of Breast Cancer in Asymptomatic and Symptomatic Groups Using Computer-Aided Detection with Full-Field Digital Mammography / 한국유방암학회지

PURPOSE: We aimed to determine the sensitivity of computer-aided detection (CAD) applied to digital mammography in asymptomatic and symptomatic breast cancer patients. METHODS: We retrospectively analyzed digital mammography and CAD images from 210 patients diagnosed with breast cancer. The patients were divided into symptomatic and asymptomatic groups. The sensitivity of CAD in both groups was assessed in relation to breast tissue density, histopathological type of breast cancer, and tumor size. RESULTS: The detection rate of the CAD system was 87.8% in the asymptomatic group. The sensitivity in different tissue densities was 100% in fatty breasts (P1), 88.9% with scattered fibroglandular densities (P2), 94.4% in heterogeneously dense breasts (P3), and 66.7% in extremely dense breasts (P4). The detection rate of the CAD system in the symptomatic group was 87.2%, and the sensitivity was 90.5%, 90%, 86.6%, and 75% in P1-P4 breasts, respectively. In the asymptomatic group, the CAD system detected 90.3% of invasive ductal carcinomas, not otherwise specified (IDC-NOS) and 88.9% of ductal carcinomas in situ (DCIS), but did not detect other types of malignancy. In the symptomatic group, the CAD system detected 88.2% of IDC-NOS, 88.9% of DCIS and 75% of other types of malignancy. When analyzed according to tumor size, the sensitivity of CAD in the asymptomatic and symptomatic groups was 82.6% and 83.3% for tumors 2 cm. CONCLUSION: The sensitivity of CAD was low in P4 breasts and high for tumors larger than 2 cm, with no statistically significant differences between the asymptomatic and symptomatic groups for IDC-NOS and DCIS. CAD showed greater sensitivity for other neoplasms in symptomatic patients.