An Analysis of the Interpretability of Neural Networks trained on Magnetic Resonance Imaging for Stroke Outcome Prediction

Applying deep learning models to MRI scans of acute stroke patients to extract features that are indicative of short-term outcome could assist a clinician’s treatment decisions. Deep learning models are usually accurate but are not easily interpretable. Here, we trained a convolutional neural network on ADC maps from hyperacute ischaemic stroke patients for prediction of short-term functional outcome and used an interpretability technique to highlight regions in the ADC maps that were most important in the prediction of a bad outcome. Although highly accurate, the model’s predictions were not based on aspects of the ADC maps related to stroke pathophysiology

Multimodal Fusion Strategies for Outcome Prediction in Stroke

Data driven methods are increasingly being adopted in the medical domain for clinical predictive modeling. Prediction of stroke outcome using machine learning could provide a decision support system for physicians to assist them in patient-oriented diagnosis and treatment. While patient-specific clinical parameters play an important role in outcome prediction, a multimodal fusion approach that integrates neuroimaging with clinical data has the potential to improve accuracy. This paper addresses two research questions: (a) does multimodal fusion aid in the prediction of stroke outcome, and (b) what fusion strategy is more suitable for the task at hand. The baselines for our experimental work are two unimodal neural architectures: a 3D Convolutional Neural Network for processing neuroimaging data, and a Multilayer Perceptron for processing clinical data. Using these unimodal architectures as building blocks we propose two feature-level multimodal fusion strategies: 1) extracted features , where the unimodal architectures are trained separately and then fused, and 2) end-to-end, where the unimodal architectures are trained together. We show that integration of neuroimaging information with clinical metadata can potentially improve stroke outcome prediction. Additionally, experimental results indicate that the end-to-end fusion approach proves to be more robust

Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores

Reliable prediction of outcomes of aneurysmal subarachnoid hemorrhage (aSAH) based on factors available at patient admission may support responsible allocation of resources as well as treatment decisions. Radiographic and clinical scoring systems may help clinicians estimate disease severity, but their predictive value is limited, especially in devising treatment strategies. In this study, we aimed to examine whether a machine learning (ML) approach using variables available on admission may improve outcome prediction in aSAH compared to established scoring systems. Combined clinical and radiographic features as well as standard scores (Hunt & Hess, WFNS, BNI, Fisher, and VASOGRADE) available on patient admission were analyzed using a consecutive single-center database of patients that presented with aSAH (n = 388). Different ML models (seven algorithms including three types of traditional generalized linear models, as well as a tree bosting algorithm, a support vector machine classifier (SVMC), a Naive Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net) were trained for single features, scores, and combined features with a random split into training and test sets (4:1 ratio), ten-fold cross-validation, and 50 shuffles. For combined features, feature importance was calculated. There was no difference in performance between traditional and other ML applications using traditional clinico-radiographic features. Also, no relevant difference was identified between a combined set of clinico-radiological features available on admission (highest AUC 0.78, tree boosting) and the best performing clinical score GCS (highest AUC 0.76, tree boosting). GCS and age were the most important variables for the feature combination. In this cohort of patients with aSAH, the performance of functional outcome prediction by machine learning techniques was comparable to traditional methods and established clinical scores. Future work is necessary to examine input variables other than traditional clinico-radiographic features and to evaluate whether a higher performance for outcome prediction in aSAH can be achieved

Comparing Poor and Favorable Outcome Prediction With Machine Learning After Mechanical Thrombectomy in Acute Ischemic Stroke

Outcome prediction after mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO) is commonly performed by focusing on favorable outcome (modified Rankin Scale, mRS 0–2) after 3 months but poor outcome representing severe disability and mortality (mRS 5 and 6) might be of equal importance for clinical decision-making

Urban Furniture in Sustainable Historical Urban Texture Landscapes: Historical Squares in the Walled City of Nicosia

Historical city textures are living history that embody public spaces of former civilizations, their landscapes, and their traditional life culture, carrying them to the present and giving them life. Squares as public spaces have been important gathering hubs for social life. Urban furniture is an important element of public spaces, and it reflects city identity and improves quality of life. Additionally, it improves the reflection of culture in historical city textures and their usability. The walled city center of Nicosia has a very rich historical texture, incorporating the cultural heritage of various civilizations in the history of Cyprus. Within the framework of texture, public spaces, which have acquired the features of squares with their functions throughout the social history of Nicosia, are still important social spaces for its community. This study analyzed Asmaaltı and Selimiye Squares, two important spaces of the walled city, through examining the urban furniture in terms of its material, shape, functionality, and conformity with the historical texture. Its deficiencies and nonconformities with the historical texture were identified accordingly. In consideration of such examples, urban furniture in historical city textures was also analyzed from the perspective of architectural styles and social, cultural, and economical characteristics, and relevant recommendations are proposed. Pursuant to the study findings, urban furniture used in both squares is not compatible with the whole traditional texture, as it is not designed in a manner that blends modern and traditional aspects. Moreover, most of it is not in good condition and will soon lose its functionality due to the lack of maintenance. Our recommendations are addressed to the relevant literature domain and historical texture in general

Kronik ve Agresif Periodontitisli Hastalarda H.pylori Açısından Subgingival Plak Analizi

Amaç: Helicobacter pylori (H. Pylori), bir gram (-), mikroaerofilik bakteri olup, kronik aktif gastrit ve peptik ülserin etyolojik faktörüdür. Bazı çalışmalar, bu bakterinin, oral kavitede bulunduğunda, mide için potansiyel rezervuar olabileceğini göstermiştir. Çeşitli çalışmalar, H. pylori'nin kronik periodontitisli hastaların tükrük ve subgingival plaklarında görülebileceğini göstermiştir. Bununla birlikte agresif periodontitis hastaları ile ilgili herhangi bir veri yoktur. Bu çalışmada, kronik, agresif periodontitis ve gingivitis hastalarının subgingival plak örneklerinde H. pylori prevalansını saptamayı ve hastaların gastrik problemler konusunda bilinçlenmesini arttırmayı amaçladık.Gereç ve Yöntem: Bu çalışma, gastrik hastalık semptomu olmayan ve son 3 ayda antibiyotik kullanmayan 155 hasta (61 adet gingivitis, 60'ı kronik periodontitisli ve 34 agresif periodontitisli) içermekteydi. Subgingival plak örnekleri steril paper point kullanılarak alındı. H. pylori, A. actinomycetemcomitans ve P. gingivalis'in varlığı RT-PCR ile tespit edildi.Bulgular: Mikrobiyolojik analizin sonunda herhangi bir grupta H. pylori tespit edilmedi. Bununla birlikte, agresif periodontit grubunda yüksek oranda A. actinomycetemcomitans (%97.1) ve P. gingivalis (%100) görülmüştür. Bununla birlikte, A. actinomycetemcomitans ve P. gingivalis, kronik periodontitisli hastaların sırasıyla %30 ve %21.7'sinde bulunmuştur. A. actinomycetemcomitans ve P. gingivalis gingivitisli hastaların %24.6'sında bulundu. Sonuç: H. pylori, örneklerde saptanmamış olması, subgingival plağın bu bakteri için birincil rezervuar olmayabileceğini gösterdi

Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores

Reliable prediction of outcomes of aneurysmal subarachnoid hemorrhage (aSAH) based on factors available at patient admission may support responsible allocation of resources as well as treatment decisions. Radiographic and clinical scoring systems may help clinicians estimate disease severity, but their predictive value is limited, especially in devising treatment strategies. In this study, we aimed to examine whether a machine learning (ML) approach using variables available on admission may improve outcome prediction in aSAH compared to established scoring systems. Combined clinical and radiographic features as well as standard scores (Hunt & Hess, WFNS, BNI, Fisher, and VASOGRADE) available on patient admission were analyzed using a consecutive single-center database of patients that presented with aSAH (n = 388). Different ML models (seven algorithms including three types of traditional generalized linear models, as well as a tree bosting algorithm, a support vector machine classifier (SVMC), a Naive Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net) were trained for single features, scores, and combined features with a random split into training and test sets (4:1 ratio), ten-fold cross-validation, and 50 shuffles. For combined features, feature importance was calculated. There was no difference in performance between traditional and other ML applications using traditional clinico-radiographic features. Also, no relevant difference was identified between a combined set of clinico-radiological features available on admission (highest AUC 0.78, tree boosting) and the best performing clinical score GCS (highest AUC 0.76, tree boosting). GCS and age were the most important variables for the feature combination. In this cohort of patients with aSAH, the performance of functional outcome prediction by machine learning techniques was comparable to traditional methods and established clinical scores. Future work is necessary to examine input variables other than traditional clinico-radiographic features and to evaluate whether a higher performance for outcome prediction in aSAH can be achieved