Cognition: development of a cognitive testing battery on the iPad for the evaluation of patients with brain Mets.

To make assessment of neurocognitive decline in patients with brain metastases more reliable and feasible, Brainlab AG developed an application 'Cognition' for the iPad by gamifying validated paper and pencil tests. This study aims at validating the computerized tests. We assessed reliability and comparability of 'Cognition' with similar well-established paper and pencil tests in two consecutive sessions per participant. The electronic tests used the same assignments with different stimuli than the paper and pencil tests. Domains involved are learning and memory, attention and processing speed, verbal fluency and executive functions. In total 5 employees and 25 cancer patients without disease in the CNS participated, of whom 24 completed both sessions. Reliability was found satisfying for the domains learning and memory (p = 0.08; p = 0.612; p = 0.4445) and verbal fluency (p = 0.064). A learning effect showed for attention and processing speed (p = 0.001) while executive functioning showed a significant decline, possibly due to radiotherapy-related fatigue (p = 0.013). Concerning comparability between electronic and paper results, a significant correlation was found for attention and processing speed (p = 0.000), for verbal fluency (p = 0.03), for executive functions (p = 0.000), but not for learning and memory (p = 0.41; p = 0.25). Overall 'Cognition' showed moderate comparability, probably caused by the consecution of tests during sessions and the unfamiliarity with electronic test in older patients. After improving its functionality, the application needs to be validated in patients with brain metastases before it can detect cognitive decline and possible early radiation toxicity or relapses.

Feasibility of ABUS as an Alternative to Handheld Ultrasound for Response Control in Neoadjuvant Breast Cancer Treatment.

INTRODUCTION: The Invenia Automated Breast Ultrasound Screening (ABUS) is indicated as an adjunct to mammography for breast cancer screening in asymptomatic women with high-density breast tissue. ABUS provides time-efficient evaluation of the 3-dimensional recordings within 3 to 6 minutes. The role and advantages of ABUS in everyday clinical practice, especially in routine examination during neoadjuvant chemotherapy (NACT), is not clear. The aim of this monocentric, noninterventional retrospective study is to evaluate the use of ABUS in patients who are under NACT treatment for response control. METHODS: Regular sonographic response check with handheld ultrasound (HHUS) examination and with ABUS were conducted in 83 women who underwent NACT. The response controls were conducted every 3 to 6 weeks during NACT. The handheld sonography was performed with GE Voluson S8. Handheld sonographic measurements and ABUS measurements were compared with the final pathologic tumor size. RESULTS: There was no statistical difference between the measurements with HHUS examination or ABUS compared with final pathologic tumor size (P = .47). The average difference from ABUS measured tumor size to final pathologic tumor size was 9.8 mm. The average difference from handheld measured tumor size to final pathologic tumor size was 9/3 mm. Both the specificity of ABUS and HHUS examination in predicting pathologic complete remission was 100%. CONCLUSION: ABUS seems to be a suitable method to conduct response control in neoadjuvant breast cancer treatment. ABUS may facilitate preoperative planning and offers remarkable time saving for physicians compared with HHUS examination and thus should be considered for clinical practice.

Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting.

Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great burden on healthcare systems. Early detection of skin tumors is expected to reduce this burden. Artificial intelligence (AI) algorithms that support skin cancer diagnoses have been shown to perform at least as well as dermatologists' diagnoses. Recognizing the need for clinically and economically efficient means of diagnosing skin cancers at early stages in the primary care attention, we developed an efficient computer-aided diagnosis (CAD) system to be used by primary care physicians (PCP). Additionally, we developed a smartphone application with a protocol for data acquisition (i.e., photographs, demographic data and short clinical histories) and AI algorithms for clinical and dermoscopic image classification. For each lesion analyzed, a report is generated, showing the image of the suspected lesion and its respective Heat Map; the predicted probability of the suspected lesion being melanoma or malignant; the probable diagnosis based on that probability; and a suggestion on how the lesion should be managed. The accuracy of the dermoscopy model for melanoma was 89.3%, and for the clinical model, 84.7% with 0.91 and 0.89 sensitivity and 0.89 and 0.83 specificity, respectively. Both models achieved an area under the curve (AUC) above 0.9. Our CAD system can screen skin cancers to guide lesion management by PCPs, especially in the contexts where the access to the dermatologist can be difficult or time consuming. Its use can enable risk stratification of lesions and/or patients and dramatically improve timely access to specialist care for those requiring urgent attention.

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance.

Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.

First proof-of-concept evaluation of the FUSION-X-US-II prototype for the performance of automated breast ultrasound in healthy volunteers.

PURPOSE: The FUSION-X-US-II prototype was developed to combine 3D-automated breast ultrasound (ABUS) and digital breast tomosynthesis in a single device without decompressing the breast. We evaluated the technical function, feasibility of the examination workflow, image quality, breast tissue coverage and patient comfort of the ABUS device of the new prototype. METHODS: In this prospective feasibility study, the FUSION-X-US-II prototype was used to perform ABUS in 30 healthy volunteers without history of breast cancer. The ABUS images of the prototype were interpreted by a physician with specialization in breast diagnostics. Any detected lesions were measured and classified using BI-RADS® scores. Image quality was rated subjectively by the physician and coverage of the breast was measured. Patient comfort was evaluated by a questionnaire after the examination. RESULTS: One hundred and six scans were performed (61 × CC, 23 × ML, 22 × MLO) in 60 breasts. Image acquisition and processing by the prototype was fast and accurate. Breast coverage by ABUS was approximately 90.8%. Sixteen breast lesions (all benign, classified as BIRADS® 2) were identified. The examination was tolerated by all patients. CONCLUSION: The FUSION-X-US-II prototype allows a rapid ABUS scan with mostly high patient comfort. Technical developments resulted in an improvement of quality and coverage compared to previous prototype versions. The results are encouraging for a test of the prototype in a clinical setting in combination with tomosynthesis.

Early screening of autism spectrum disorder using cry features.

The increase in the number of children with autism and the importance of early autism intervention has prompted researchers to perform automatic and early autism screening. Consequently, in the present paper, a cry-based screening approach for children with Autism Spectrum Disorder (ASD) is introduced which would provide both early and automatic screening. During the study, we realized that ASD specific features are not necessarily observable in all children with ASD and in all instances collected from each child. Therefore, we proposed a new classification approach to be able to determine such features and their corresponding instances. To test the proposed approach a set of data relating to children between 18 to 53 months which had been recorded using high-quality voice recording devices and typical smartphones at various locations such as homes and daycares was studied. Then, after preprocessing, the approach was used to train a classifier, using data for 10 boys with ASD and 10 Typically Developed (TD) boys. The trained classifier was tested on the data of 14 boys and 7 girls with ASD and 14 TD boys and 7 TD girls. The sensitivity, specificity, and precision of the proposed approach for boys were 85.71%, 100%, and 92.85%, respectively. These measures were 71.42%, 100%, and 85.71% for girls, respectively. It was shown that the proposed approach outperforms the common classification methods. Furthermore, it demonstrated better results than the studies which used voice features for screening ASD. To pilot the practicality of the proposed approach for early autism screening, the trained classifier was tested on 57 participants between 10 to 18 months. These 57 participants consisted of 28 boys and 29 girls and the results were very encouraging for the use of the approach in early ASD screening.

Theoretical Background to Automated Diagnosing of Oral Leukoplakia: A Preliminary Report.

Oral leukoplakia represents the most common oral potentially malignant disorder, so early diagnosis of leukoplakia is important. The aim of this study is to propose an effective texture analysis algorithm for oral leukoplakia diagnosis. Thirty-five patients affected by leukoplakia were included in this study. Intraoral photography of normal oral mucosa and leukoplakia were taken and processed for texture analysis. Two features of texture, run length matrix and co-occurrence matrix, were analyzed. Difference was checked by ANOVA. Factor analysis and classification by the artificial neural network were performed. Results revealed easy possible differentiation leukoplakia from normal mucosa (p < 0.05). Neural network discrimination shows full leukoplakia recognition (sensitivity 100%) and specificity 97%. This objective analysis in the neural network revealed that involving 3 textural features into optical analysis of the oral mucosa leads to proper diagnosis of leukoplakia. Application of texture analysis for leukoplakia is a promising diagnostic method.

A Noninvasive Assistant System in Diagnosis of Lumbar Disc Herniation.

The purpose of this study is the application of pressure sensors in diagnostics and evaluation of the accuracy diagnostics of lumbar disc herniation at levels L4/L5 and L5/S1 using the aforementioned platform. The motivation behind the idea to apply the pressure measurement platform is the fact that the motor weakness of plantar and dorsal flexia of the feet is one of the absolute indications for the operative treatment of patients with lumbar disc herniation at the indicated levels. In patients, MRI diagnosis of the lumbosacral spine served as the ground truth in the diagnosis of herniation at L4/L5 and L5/S1 levels. The inclusive criteria for the study were the proven muscle weakness based on manual muscle tests performed prior to surgery, after seven days of surgery and after physical therapy. The results obtained with the manual muscular test were compared with the results obtained using our platform. The study included 33 patients who met the inclusion criteria. The results of the measurements indicate that the application of our platform with pressure sensors has the same sensitivity diagnostics as a manual muscle test, when done preoperatively and postoperatively. After physical therapy, pressure sensors show statistically significantly better sensitivity compared to the clinical manual muscle test. The obtained results are encouraging in the sense that the pressure platform can be an additional diagnostic method for lumbar disc herniation detection and can indicate the effectiveness of operative treatment and physical therapy after operation. The main advantage of the system is the cost; the whole system with platform and sensors is not expensive.

Assessing Arm Volume in People During and After Treatment for Breast Cancer: Reliability and Convergent Validity of the LymphaTech System.

BACKGROUND: There are challenges related to the accurate and efficient measurement of lymphedema in people with breast cancer. The LymphaTech 3D Imaging System (LymphaTech, Atlanta, GA, USA) is a mobile, noninvasive platform that provides limb geometry measurements. OBJECTIVE: The objective of this study was to estimate the reliability and validity of the LymphaTech for measuring arm volume in the context of women seeking care in a specialty breast cancer rehabilitation clinic. DESIGN: This was a cross-sectional reliability and convergent validity study. METHODS: People who had stage I to IV breast cancer with lymphedema or were at risk for it were included. Arm volume was measured in 66 participants using the LymphaTech and perometer methods. Test-retest reliability for a single measure, limb volume difference, and agreement between methods was analyzed for 30 participants. A method-comparison analysis was also used to assess convergent validity between methods. RESULTS: Both LymphaTech and perometer methods displayed intraclass correlation coefficients (ICCs) of ≥0.99. The standard errors of measurement for the LymphaTech and length-matched perometer measurements were nearly identical. Similar intraclass correlation coefficients (0.97) and standard errors of measurement (38.0-40.7 mL) were obtained for the between-limb volume difference for both methods. The convergent validity analyses demonstrated no systematic difference between methods. LIMITATIONS: The sample size was not based on a formal sample size calculation. LymphaTech measurements included interrater variance, and perometer measurements contained intrarater variance. CONCLUSIONS: The LymphaTech had excellent test-retest reliability, and convergent validity was supported. This technology is efficient and portable and has a potential role in prospective surveillance and management of lymphedema in clinical, research, and home settings.

Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.

BACKGROUND: Colonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by providing visual alarms during the procedure. We aimed to assess the effectiveness of a CADe system that avoids potential operational bias. METHODS: We did a double-blind randomised trial at the endoscopy centre in Caotang branch hospital of Sichuan Provincial People's Hospital in China. We enrolled consecutive patients (aged 18-75 years) presenting for diagnostic and screening colonoscopy. We excluded patients with a history of inflammatory bowel disease, colorectal cancer, or colorectal surgery or who had a contraindication for biopsy; we also excluded patients who had previously had an unsuccessful colonoscopy and who had a high suspicion for polyposis syndromes, inflammatory bowel disease, and colorectal cancer. We allocated patients (1:1) to colonoscopy with either the CADe system or a sham system. Randomisation was by computer-generated random number allocation. Patients and the endoscopist were unaware of the random assignment. To achieve masking, the output of the system was shown on a second monitor that was only visible to an observer who was responsible for reporting the alerts. The primary outcome was the adenoma detection rate (ADR), which is the proportion of individuals having a complete colonoscopy, from caecum to rectum, who had one or more adenomas detected. The primary analysis was per protocol. We also analysed characteristics of polyps and adenomas missed initially by endoscopists but detected by the CADe system. This trial is complete and is registered with http://www.chictr.org.cn, ChiCTR1800017675. FINDINGS: Between Sept 3, 2018, and Jan 11, 2019, 1046 patients were enrolled to the study, of whom 36 were excluded before randomisation, 508 were allocated colonoscopy with polyp detection using the CADe system, and 502 were allocated colonoscopy with the sham system. After further excluding patients who met exclusion criteria, 484 patients in the CADe group and 478 in the sham group were included in analyses. The ADR was significantly greater in the CADe group than in the sham group, with 165 (34%) of 484 patients allocated to the CADe system having one or more adenomas detected versus 132 (28%) of 478 allocated to the sham system (odds ratio 1·36, 95% CI 1·03-1·79; p=0·030). No complications were reported among all colonoscopy procedures. Polyps initially missed by the endoscopist but identified by the CADe system were generally small in size, isochromatic, flat in shape, had an unclear boundary, were partly behind colon folds, and were on the edge of the visual field. INTERPRETATION: Polyps initially missed by the endoscopist had characteristics that are sometimes difficult for skilled endoscopists to recognise. Such polyps could be detected using a high-performance CADe system during colonoscopy. The effect of CADe during colonoscopy on the incidence of interval colorectal cancer should be investigated. FUNDING: None.

Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware.

Automatic decision support systems have gained importance in health sector in recent years. In parallel with recent developments in the fields of artificial intelligence and image processing, embedded systems are also used in decision support systems for tumor diagnosis. Extreme learning machine (ELM), is a recently developed, quick and efficient algorithm which can quickly and flawlessly diagnose tumors using machine learning techniques. Similarly, significantly fast and robust fuzzy C-means clustering algorithm (FRFCM) is a novel and fast algorithm which can display a high performance. In the present study, a brain tumor segmentation approach is proposed based on extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms (BTS-ELM-FRFCM) running on Raspberry Pi (PRI) hardware. The present study mainly aims to introduce a new segmentation system hardware containing new algorithms and offering a high level of accuracy the health sector. PRI's are useful mobile devices due to their cost-effectiveness and satisfying hardware. 3200 training images were used to train ELM in the present study. 20 pieces of MRI images were used for testing process. Figure of merid (FOM), Jaccard similarity coefficient (JSC) and Dice indexes were used in order to evaluate the performance of the proposed approach. In addition, the proposed method was compared with brain tumor segmentation based on support vector machine (BTS-SVM), brain tumor segmentation based on fuzzy C-means (BTS-FCM) and brain tumor segmentation based on self-organizing maps and k-means (BTS-SOM). The statistical analysis on FOM, JSC and Dice results obtained using four different approaches indicated that BTS-ELM-FRFCM displayed the highest performance. Thus, it can be concluded that the embedded system designed in the present study can perform brain tumor segmentation with a high accuracy rate.

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)

Multilayered rule-based expert system for diagnosing uveitis.

Uveitis is a condition caused by inflammation of the uvea, which is the middle layer of the eye. Uveitis can result in swelling or destruction of the eye tissue, which can lead to visual impairment or blindness [1]. Many diseases, either systemic or localized to the eye, are associated with the symptoms of uveitis. Thus, it is often hard to determine the underlying disease responsible for uveitis, especially when the signs and symptoms are unclear. Additionally, there are few experts on uveitis, especially in poor and developing countries. In this paper, we design and build a rule-based expert system to diagnose uveitis. The main motivation for developing this expert system was to mitigate the lack of human experts by helping general ophthalmologists achieve a correct diagnosis with minimal time and effort. Furthermore, the system can act as a good educational tool for newly graduated doctors, guiding their work with their patients and supporting their diagnostic decisions. The novel multilayer design of the system allows flexibility and ease of scaling to new cases in the future. Many techniques were used to improve the system's diagnostic flexibility and overcome incomplete user input. Tests of the system have yielded promising results.

Recognition and Clinical Diagnosis of Cervical Cancer Cells Based on our Improved Lightweight Deep Network for Pathological Image.

Accurate recognition of cervical cancer cells is of great significance to clinical diagnosis, but these existing algorithms are designed by low-level manual feature, and their performance improvements are limited an improved algorithm based on residual neural network is proposed to improve the accuracy of diagnosis. Firstly, momentum parameters are introduced into the training model; secondly, by changing the number of training samples, the recognition rate of the algorithm can be improved. Therefore, aiming at the task of object recognition under resource constrained condition, we optimize the design method of the network structure such as convolution operation, model parameter compression and enhancement of feature expression depth, and design and implement the lightweight network model structure for embedded platform. Our proposed deep network model can reduce the parameters of the model and the resources needed for operation under the condition of guaranteeing the precision. The experimental results show that the lightweight deep model has better performance than that of the existing comparison models, and it can achieve the model accuracy of 94.1% under the condition that the model with fewer parameters on cervical cells data set.

Decrease in interpretation time for both novice and experienced readers using a concurrent computer-aided detection system for digital breast tomosynthesis.

OBJECTIVES: To compare the diagnostic performance and interpretation time of digital breast tomosynthesis (DBT) for both novice and experienced readers with and without using a computer-aided detection (CAD) system for concurrent read. METHODS: CAD system was developed for concurrent read in DBT interpretation. In this observer performance study, we used an enriched sample of 100 DBT cases including 70 with and 30 without breast cancers. Image interpretation was performed by four radiologists with different experience levels (two experienced and two novice). Each reader completed two reading sessions (at a minimum 2-month interval), once with and once without CAD. Three different rating scales were used to record each reader's interpretation. Reader performance with and without CAD was reported and compared for each radiologist. Reading time for each case was also recorded. RESULTS: Average area under the receiver operating characteristic curve values for BI-RADS scale on using CAD were 0.778 and 0.776 without using CAD, demonstrating no statistically significant differences. Results were consistent when the probability of malignancy and percentage probability of malignancy scales were used. Reading times per case were 72.07 s and 62.03 s (SD, 37.54 s vs 34.38 s) without and with CAD, respectively. The average difference in reading time on using CAD was a statistically significant decrease of 10.04 ± 1.85 s, providing 14% decrease in time. The time-reducing effect was consistently observed in both novice and experienced readers. CONCLUSION: DBT combined with CAD reduced interpretation time without diagnostic performance loss to novice and experienced readers. KEY POINTS: • The use of a concurrent DBT-CAD system shortened interpretation time. • The shortened interpretation time with DBT-CAD did not come at a cost to diagnostic performance to novice or experienced readers. • The concurrent DBT-CAD system improved the efficiency of DBT interpretation.

Recent advancement in the early detection of melanoma using computerized tools: An image analysis perspective.

BACKGROUND: The paper reviews the advancement of tools and current technologies for the detection of melanoma. We discussed several computational strategies from pre- to postprocessing image operations, descriptors, and popular classifiers to diagnose a suspected skin lesion based on its virtual similarity to the malignant lesion with known histopathology. We reviewed the current state of smart phone-based apps as diagnostic tools for screening. METHODS: A literature survey was conducted using a combination of keywords in the bibliographic databases: PubMed, AJCC, PH2, EDRA, and ISIC melanoma project. A number of melanoma detection apps were downloaded for two major mobile operating systems, iOS and Android; their important uses, key challenges, and various expert opinions were evaluated and also discussed. RESULTS: We have provided an overview of research on the computer-aided diagnosis methods to estimate melanoma risk and early screening. Dermoscopic images are the most viable option for the advent of new image processing technologies based on which many of the skin cancer detection apps are being developed recently. We have categorized and explored their potential uses, evaluation criteria, limitations, and other details. CONCLUSION: Such advancements are helpful in the sense they are raising awareness. Diagnostic accuracy is the major issue of smart phone-based apps and it cannot replace an adequate clinical experience and biopsy procedures.

Breast Cancer Prediction Using Dominance-based Feature Filtering Approach: A Comparative Investigation in Machine Learning Archetype

Abstract Breast cancer is the most commonly witnessed cancer amongst women around the world. Computer aided diagnosis (CAD) have been playing a significant role in early detection of breast tumors hence to curb the overall mortality rate. This work presents an enhanced empirical study of impact of dominance-based filtering approach on performances of various state-of-the-art classifiers. The feature dominance level is proportional to the difference in means of benign and malignant tumors. The experiments were done on original Wisconsin Breast Cancer Dataset (WBCD) with total nine features. It is found that the classifiers' performances for top 4 and top 5 dominant-based features are almost equivalent to performances for all nine features. Artificial neural network (ANN) is come forth as the best performing classifier among all with accuracies of 98.9% and 99.6% for top 4 and top 5 dominant features respectively. The error rate of ANN between all nine and top 4 &5 dominant features is less than 2% for four performance evaluation parameters namely sensitivity, specificity, accuracy and AUC. Thus, it can be stated that the dominance-based filtering approach is appropriate for selecting a sound set of features from the feature pool, consequently, helps to reduce computation time with no deterioration in classifier's performance.

A Novel Run-length based wavelet features for Screening Thyroid Nodule Malignancy

Abstract: Thyroid nodules are cell growths in the thyroid which might be for in one of two categories benign or malignant. Nodular thyroid disease is common and because of the associated risk of malignancy and hyper-function; these nodules have to be examined thoroughly. Hence diagnosing thyroid nodule malignancy in the early stage can mitigate the possibility of death. This paper presents an intelligent thyroid nodules malignancy diagnosis using texture information in run-length matrix derived from 2- level 2D wavelet transform bands (approximation and details). In this work, ANOVA test has been used to for feature selection to reduce for feature selection about 45 run-length features with and without wavelet generated, before feeding those features which clinical importance to the Support Vector Machine(SVM) and Decision Tree (DT) classifier to perform the automated diagnosis. The validation of this work is activated using 100-thyroid nodule images spliced equally between the two categories (50 Benign and 50 Malignant). The proposed system can detect thyroid nodules malignancy with an average accuracy of about 97% using SVM classifier for the run- length matrix, features derived from spatial domain while the average accuracy is increased to 98% in case of hybrid feature derived from spatial domain and 2-level wavelet decomposition. For the other proposed classifier (DT), the average accuracy in case of spatial domain based features is 93% whereas the average accuracy of the hybrid features system is 97%. Hence the proposed system can be used for the screening of thyroid nodules.

Learning-based classification of informative laryngoscopic frames.

BACKGROUND AND OBJECTIVE: Early-stage diagnosis of laryngeal cancer is of primary importance to reduce patient morbidity. Narrow-band imaging (NBI) endoscopy is commonly used for screening purposes, reducing the risks linked to a biopsy but at the cost of some drawbacks, such as large amount of data to review to make the diagnosis. The purpose of this paper is to present a strategy to perform automatic selection of informative endoscopic video frames, which can reduce the amount of data to process and potentially increase diagnosis performance. METHODS: A new method to classify NBI endoscopic frames based on intensity, keypoint and image spatial content features is proposed. Support vector machines with the radial basis function and the one-versus-one scheme are used to classify frames as informative, blurred, with saliva or specular reflections, or underexposed. RESULTS: When tested on a balanced set of 720 images from 18 different laryngoscopic videos, a classification recall of 91% was achieved for informative frames, significantly overcoming three state of the art methods (Wilcoxon rank-signed test, significance level = 0.05). CONCLUSIONS: Due to the high performance in identifying informative frames, the approach is a valuable tool to perform informative frame selection, which can be potentially applied in different fields, such us computer-assisted diagnosis and endoscopic view expansion.

Comparison Between Duplex Ultrasound and Multigate Quality Doppler Profile Software in the Assessment of Lower Limb Perforating Vein Direction.

OBJECTIVES: The aim was to assess more accurately the net flow of the lower limb perforating veins (PVs). MATERIAL AND METHODS: This was an observational prospective study. Two hundred and twenty one limbs with chronic venous disease (C1-6EpAs,pPr) of 193 patients underwent a duplex ultrasound (DUS). All identified PVs were scanned also by means of quality Doppler profile (QDP) multigate analysis in order to determine their net inward and outward flow direction. A comparison between the traditional pulsed wave Doppler analysis and QDP was performed to detect potential discrepancy between the traditional definition of PV incompetence and a net outward flow. RESULTS: The DUS investigation identified 774 PVs. Only 7.7% of the PVs showed an outward flow lasting more than 500 ms. Among the PVs showing a longer than 500 ms outward flow, QDP assessment revealed net outward flow in only 84% of the PVs along the thigh and in 28.6% along the lower leg. Among the PVs showing a shorter than 500 ms outward flow, QDP assessment reported a net outward flow in 2.4% of the PVs along the thigh and in 47.3% of those along the lower leg. The sensitivity of an outward flow lasting more than 500 ms in detecting an actual net outward flow was 13.9% (9-20.1%). The specificity of an outward flow lasting less than 500 ms in detecting a net inward flow was 96.4% (93.2-98.3%). CONCLUSIONS: A lack of overlap exists between the finding of a PV outward flow lasting more than 500 ms and the net outward flow of the same vessel. The traditional definition of PV incompetence is challenged by the reported data and further investigations are required to identify a gold standard assessment.