scholarly journals Planning sEEG implantation using automated lesion detection: retrospective feasibility study

2019 ◽  
Author(s):  
Konrad Wagstyl ◽  
Sophie Adler ◽  
Birgit Pimpel ◽  
Aswin Chari ◽  
Kiran Seunarine ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structural Mri ◽  
Cross Sectional Study ◽  
Lesion Detection ◽  
Seizure Onset ◽  
Cross Sectional ◽  
Seizure Onset Zone ◽  
Cortical Dysplasias ◽  
Drug Resistant Epilepsy ◽  
High Degree

AbstractObjectiveA retrospective, cross-sectional study to evaluate the feasibility and potential benefits of incorporating deep-learning on structural MRI into planning stereoelectroencephalography (sEEG) implantation in paediatric patients with diagnostically complex drug-resistant epilepsy. This study aims to assess the degree of co-localisation between automated lesion detection and the seizure onset zone (SOZ) as assessed by sEEG.MethodsA neural network classifier was applied to cortical features from MRI data from three cohorts. 1) The network was trained and cross-validated using 34 patients with visible focal cortical dysplasias (FCDs). 2) Specificity was assessed in 20 paediatric healthy controls. 3) Feasibility for incorporation into sEEG implantation plans was evaluated in 38 sEEG patients. Coordinates of sEEG contacts were coregistered with classifier-predicted lesions. sEEG contacts in seizure onset and irritative tissue were identified by clinical neurophysiologists. A distance of <10mm between SOZ contacts and classifier-predicted lesions was considered co-localisation.ResultsIn patients with radiologically-defined lesions, classifier sensitivity was 74% (25/34 lesions detected). No clusters were detected in the controls (specificity 100%). Of 34 sEEG patients, 21 patients had a focal cortical SOZ. Of these there was co-localisation between classifier output and SOZ contacts in 62%. The algorithm detected 7/8 histopathologically-confirmed FCDs (86%).ConclusionsThere was a high degree of co-localisation between automated lesion detection and sEEG. We have created a framework for incorporation of deep-learning based MRI lesion detection into sEEG implantation planning. Our findings demonstrate that automated MRI analysis could be used to plan optimal electrode trajectories.

scholarly journals Suicidal Ideation Profiles in Patients with Fibromyalgia Using Transdiagnostic Psychological and Fibromyalgia-Associated Variables

2020 ◽  
Vol 18 (1) ◽  
pp. 209
Author(s):  
Jorge L. Ordóñez-Carrasco ◽  
María Sánchez-Castelló ◽  
Elena P. Calandre ◽  
Isabel Cuadrado-Guirado ◽  
Antonio J. Rojas-Tejada
Keyword(s):  
Suicidal Ideation ◽  
High Risk ◽  
Suicide Risk ◽  
Fibromyalgia Impact Questionnaire ◽  
Cross Sectional Study ◽  
Psychological Pain ◽  
Cross Sectional ◽  
Psychological Variables ◽  
Interpersonal Needs Questionnaire ◽  
High Degree

Several studies have emphasized the heterogeneity of fibromyalgia patients. Furthermore, fibromyalgia patients are considered a high-risk suicide group. The ideation-to-action framework proposes a set of transdiagnostic psychological factors involved in the development of suicidal ideation. The present study aims to explore the existence of different subgroups according to their vulnerability to suicidal ideation through these transdiagnostic psychological variables and a set of variables typically associated with fibromyalgia. In this cross-sectional study, 151 fibromyalgia patients were assessed through the Revised Fibromyalgia Impact Questionnaire, Beck Depression Inventory-II, Plutchik Suicide Risk Scale, Interpersonal Needs Questionnaire, Defeat Scale, Entrapment Scale, Psychache Scale, and Beck Hopelessness Scale. A K-means cluster analysis identified two clusters, one (45.70%) according to a low vulnerability, and a second (54.30%) with a high vulnerability to suicidal ideation. These clusters showed statistically significant differences in suicidal ideation and suicide risk. However, no differences were observed in most socio-demographic variables. In conclusion, fibromyalgia patients who present a clinical condition characterized by a moderate-high degree of physical dysfunction, overall disease impact and intensity of fibromyalgia-associated symptoms, along with a high degree of perceived burdensomeness, thwarted belongingness, defeat, entrapment, psychological pain and hopelessness, form a homogeneous group at high risk for suicidal ideation.

scholarly journals Are surface and deep learning approaches associated with study patterns and choices among medical students? A cross-sectional study

2018 ◽  
Vol 136 (5) ◽  
pp. 414-420 ◽  
Author(s):  
Álvaro Henrique de Almeida Delgado ◽  
João Paulo Rodrigues Almeida ◽  
Larissa Souza Borowski Mendes ◽  
Isabella Noceli de Oliveira ◽  
Oscarina da Silva Ezequiel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Students ◽  
Cross Sectional Study ◽  
Learning Approaches ◽  
Sectional Study ◽  
Cross Sectional

scholarly journals Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

PLoS Medicine ◽  
2018 ◽  
Vol 15 (11) ◽  
pp. e1002683 ◽  
Author(s):  
John R. Zech ◽  
Marcus A. Badgeley ◽  
Manway Liu ◽  
Anthony B. Costa ◽  
Joseph J. Titano ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Cross Sectional Study ◽  
Chest Radiographs ◽  
Sectional Study ◽  
Cross Sectional ◽  
Generalization Performance ◽  
Deep Learning Model

scholarly journals The Politics of Security Deployment of Security Operatives to Jos Metropolis, Plateau State, Nigeria 2001-2014

2017 ◽  
Vol 17 (1) ◽  
pp. 95-132
Author(s):  
Umar Dantani ◽  
Peter Nungshak Wika ◽  
Muhammad Maigari Abdullahi
Keyword(s):  
Ethnic Groups ◽  
Task Force ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Security Personnel ◽  
Key Informant ◽  
Plateau State ◽  
High Degree ◽  
Depth Interviews

Abstract The paper examines the politics of security deployment by the Federal Government of Nigeria to Jos, metropolis. A cross-sectional study was conducted and Public Opinion Theory adopted. Methodologically, mixed methods of data collection were conducted that involved the administration of 377 questionnaires to adult respondents, six In-Depth Interviews with religious and community leaders while three Key Informant Interviews with security personnel working with Special Task Force. The survey reveals that, the deployment of Mobile Police from 2001-2010 and the formation of Special Task Force in 2010 has generated mixed reactions and divergent perceptions among the residents of Jos metropolis. Majority of the ethnic groups that are predominantly Christians were more contented with the deployment of the Mobile Police whereas ethnic groups that are dominantly Muslims questioned the neutrality, capability, performance and strength of the Nigerian Police Force in managing the crises. The study recommends that, security personnel should display high degree of neutrality in order to earn the confidence of the residents and change their perceptions.

Screening children at risk for developmental disabilities based on face landmark from video data of mobile-based application: Cross-Sectional Study (Preprint)

2021 ◽  
Author(s):  
Yu Rang Park ◽  
Sang Ho Hwang ◽  
Yeonsoo Yu ◽  
Jichul Kim ◽  
Taeyeop Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Developmental Disabilities ◽  
Early Detection ◽  
Short Term Memory ◽  
Preliminary Evidence ◽  
Cross Sectional Study ◽  
Important Variable ◽  
Video Data ◽  
Classification Model ◽  
Cross Sectional

BACKGROUND Early detection and intervention of developmental disabilities (DDs) are critical for improving the long-term outcomes of the afflicted children. Mobile-based applications are easily accessible and may thus help the early identification of DDs. OBJECTIVE We aimed to identify facial expression and head pose based on face landmark data extracted from face recording videos and to differentiate the characteristics between children with DDs and those without. METHODS Eighty-nine children (DD, n=33; typically developing, n=56) were included in the analysis. Using the mobile-based application, we extracted facial landmarks and head poses from the recorded videos and performed Long Short-Term Memory(LSTM)-based DD classification. RESULTS Stratified k-fold cross-validation showed that the average values of accuracy, precision, recall, and f1-score of the LSTM based deep learning model of DD children were 88%, 91%,72%, and 80%, respectively. Through the interpretation of prediction results using SHapley Additive exPlanations (SHAP), we confirmed that the nodding head angle variable was the most important variable. All of the top 10 variables of importance had significant differences in the distribution between children with DDs and those without (p<0.05). CONCLUSIONS Our results provide preliminary evidence that the deep-learning classification model using mobile-based children’s video data could be used for the early detection of children with DDs.

Contamination of swimming pools and hot tubs biofilms with Acanthamoeba

2018 ◽  
Vol 63 (1) ◽  
pp. 147-153 ◽  
Author(s):  
Behroz Mahdavi Poor ◽  
Abdolhossein Dalimi ◽  
Fatemeh Ghafarifar ◽  
Fariba Khoshzaban ◽  
Jalal Abdolalizadeh
Keyword(s):  
Pcr Amplification ◽  
Cross Sectional Study ◽  
Recreational Water ◽  
Swimming Pools ◽  
Cross Sectional ◽  
Swimming Pool Water ◽  
Specific Fragment ◽  
Hot Tubs ◽  
High Degree ◽  
Tabriz City

Abstract The members of Acanthamoeba genus are ubiquitous amoeba which could be a pathogenic parasite. The amoeba is resistant to the common chlorine concentration that used for disinfecting the swimming pool water. Therefore, the pools can be suitable environments for the survival and multiplication of the amoeba. In this cross sectional study, 10 indoor recreational water centers from different regions of Tabriz city were selected and sampling was done from fixed and floating biofilms of the swimming pools and hot tubs. The samples were cultured and monitored for the presence of amoeba cyst or trophozoite. For molecular identification of Acanthamoeba, PCR (polymerase chain reaction) and sequencing were conducted based on genus specific fragment of 18S ribosomal DNA (Rns). Acanthamoeba contamination was observed in 6 centers of 10 recreational centers. Based on the amoeba isolation from fixed and floating biofilms, 2 (20%) swimming pools, and 5 (50%) hot tubs were contaminated. Based on the type of the sample, the highest contamination was found in the hot tub water (40%) and the least was found in the swimming pools water (10%) and fixed biofilms of the swimming pools (10%). Out of 8 isolates, 5 (62.5%) were shown expected product in PCR amplification. Sequence analysis showed that Acanthamoeba isolates belonged to the T3 and T4 genotypes. The study revealed a high degree of contamination in the indoor recreational water centers in Tabriz city. So, it is essential to pay closer attention to the hygiene of swimming pools and hot tubs.

scholarly journals Reducing the number of unnecessary biopsies of US-BI-RADS 4a lesions through a deep learning method for residents-in-training: a cross-sectional study

BMJ Open ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. e035757
Author(s):  
Chenyang Zhao ◽  
Mengsu Xiao ◽  
He Liu ◽  
Ming Wang ◽  
Hongyan Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Imaging ◽  
Diagnostic Performance ◽  
Intraclass Correlation ◽  
Cross Sectional Study ◽  
Computer Assisted ◽  
Sectional Study ◽  
Benign Lesions ◽  
Cross Sectional ◽  
Experienced Radiologist

ObjectiveThe aim of the study is to explore the potential value of S-Detect for residents-in-training, a computer-assisted diagnosis system based on deep learning (DL) algorithm.MethodsThe study was designed as a cross-sectional study. Routine breast ultrasound examinations were conducted by an experienced radiologist. The ultrasonic images of the lesions were retrospectively assessed by five residents-in-training according to the Breast Imaging Report and Data System (BI-RADS) lexicon, and a dichotomic classification of the lesions was provided by S-Detect. The diagnostic performances of S-Detect and the five residents were measured and compared using the pathological results as the gold standard. The category 4a lesions assessed by the residents were downgraded to possibly benign as classified by S-Detect. The diagnostic performance of the integrated results was compared with the original results of the residents.ParticipantsA total of 195 focal breast lesions were consecutively enrolled, including 82 malignant lesions and 113 benign lesions.ResultsS-Detect presented higher specificity (77.88%) and area under the curve (AUC) (0.82) than the residents (specificity: 19.47%–48.67%, AUC: 0.62–0.74). A total of 24, 31, 38, 32 and 42 identified as BI-RADS 4a lesions by residents 1, 2, 3, 4 and 5 were downgraded to possibly benign lesions by S-Detect, respectively. Among these downgraded lesions, 24, 28, 35, 30 and 40 lesions were proven to be pathologically benign, respectively. After combining the residents' results with the results of the software in category 4a lesions, the specificity and AUC of the five residents significantly improved (specificity: 46.02%–76.11%, AUC: 0.71–0.85, p<0.001). The intraclass correlation coefficient of the five residents also increased after integration (from 0.480 to 0.643).ConclusionsWith the help of the DL software, the specificity, overall diagnostic performance and interobserver agreement of the residents greatly improved. The software can be used as adjunctive tool for residents-in-training, downgrading 4a lesions to possibly benign and reducing unnecessary biopsies.

scholarly journals High Intensity Physical Exercise and Pain in the Neck and Upper Limb among Slaughterhouse Workers: Cross-Sectional Study

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Emil Sundstrup ◽  
Markus D. Jakobsen ◽  
Kenneth Jay ◽  
Mikkel Brandt ◽  
Lars L. Andersen
Keyword(s):  
Physical Exercise ◽  
Musculoskeletal Disorders ◽  
Upper Limb ◽  
High Intensity ◽  
Cross Sectional Study ◽  
General Reference ◽  
Cross Sectional ◽  
Work Related ◽  
Upper Limb Movements ◽  
High Degree

Slaughterhouse work involves a high degree of repetitive and forceful upper limb movements and thus implies an elevated risk of work-related musculoskeletal disorders. High intensity strength training effectively rehabilitates musculoskeletal disorders among sedentary employees, but less is known about the effect among workers with repetitive and forceful work demands. Before performing randomized controlled trials it may be beneficial to assess the cross-sectional connection between exercise and musculoskeletal pain. We investigated the association between high intensity physical exercise and pain among 595 slaughterhouse workers in Denmark, Europe. Using logistic regression analyses, odds ratios for pain and work disability as a function of physical exercise, gender, age, BMI, smoking, and job position were estimated. The prevalence of pain in the neck, shoulder, elbow, and hand/wrist was 48%, 60%, 40%, and 52%, respectively. The odds for experiencing neck pain were significantly lower among slaughterhouse workers performing physical exercise (OR = 0.70, CI: 0.49–0.997), whereas the odds for pain in the shoulders, elbow, or hand/wrist were not associated with exercise. The present study can be used as general reference of pain in the neck and upper extremity among slaughterhouse workers. Future studies should investigate the effect of high intensity physical exercise on neck and upper limb pain in slaughterhouse workers.

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