Morphological Diagnosis and Treatment Decisions

2006 ◽  
pp. 165-177
Author(s):  
Albert Singer
Keyword(s):  
Treatment Decisions ◽  
Diagnosis And Treatment ◽  
Morphological Diagnosis

Abstract NS6: Born to Run: Advanced Practice Provider-Led Mobile Stroke Unit Care Measures Up to Vascular Neurologists’ Diagnosis and Management

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Anne W Alexandrov ◽  
Wendy Dusenbury ◽  
Victoria Swatzell ◽  
Joseph Rike ◽  
Andrew Bouche ◽  
...  
Keyword(s):  
Medical Diagnosis ◽  
Treatment Decisions ◽  
Diagnosis And Treatment ◽  
Advanced Practice ◽  
Treatment Delivery ◽  
Stroke Treatment ◽  
Diagnosis And Management ◽  
Mobile Stroke Unit ◽  
Stroke Mimic ◽  
Treatment Safety

Background: Mobile Stroke Units (MSU) are growing in numbers throughout the U.S. and abroad, with numerous staffing configurations, telemedicine, and differing imaging capabilities. We aimed to test the diagnostic accuracy and treatment safety, alongside time to diagnosis and treatment delivery of a novel advanced practice provider (APP) led MSU team. Methods: We launched an MSU housing a hospital-grade Siemens Somatom CT with CTA capabilities, and hired APPs with advanced neurovascular practitioner board certification to lead field medical diagnosis and order/initiate treatment for encountered stroke patients. Consecutive MSU patients were evaluated for differences between APPs and Vascular Neurologists (VNs) diagnosis and management, and scene diagnosis and treatment times were collected. Results: Agreement between APP field medical diagnosis and MD hospital diagnosis was 100%; stroke mimic diagnosis agreement was 98%. Overall agreement for field interpretation of CT/CTA was 97%, with discrepancies not associated with stroke treatment decisions. MDs’ agreement with APPs’ identification/treatment of ICH was 100%, and IVtPA treatment decisions 98% (APPs more conservative). Scene arrival to medical diagnosis (including clinical exam and imaging completion/interpretation) ranged from 7-10 minutes, of which 4 minutes were CT/CTA start to finish times. Scene arrival to IVtPA bolus ranged from 16 minutes to 33 minutes and was driven primarily by need for control of excessive hypertension, with scene arrival to start of nicardipine premix infusion ranging from 10-14 minutes. Conclusions: Use of an APP-led MSU is safe and non-inferior to VN diagnosis/management, and may be faster than telemedicine guided MSU treatment.

Evaluation of ankle conventional x-ray as a gold standard on diagnosis and treatment decisions for ankle weber B fractures. Self-controlled multi-centric experiment

2020 ◽  
Vol 34 (3) ◽  
pp. 201-211
Author(s):  
Sergio Camilo Espinoza-Azula ◽  
Eduardo Antonio Reina-Valdivieso ◽  
Bosco Mendoza ◽  
Victor Toledo-Infanson ◽  
Carlos Ramirez ◽  
...  
Keyword(s):  
Gold Standard ◽  
Treatment Decisions ◽  
Diagnosis And Treatment ◽  
X Ray

A fuzzy based high-resolution multi-view deep CNN for breast cancer diagnosis through SVM classifier on visual analysis

2020 ◽  
Vol 39 (6) ◽  
pp. 8573-8586
Author(s):  
Sudhakar Sengan ◽  
V. Priya ◽  
A. Syed Musthafa ◽  
Logesh Ravi ◽  
Saravanan Palani ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
High Resolution ◽  
Cancer Diagnosis ◽  
Medical Images ◽  
Breast Cancer Diagnosis ◽  
Treatment Decisions ◽  
Diagnosis And Treatment ◽  
Automated System ◽  
Svm Classifier

Breast cancer should be diagnosed as early as possible. A new approach of the diagnosis using deep learning for breast cancer and the particular process using segmentation strategies presented in this article. Medical imagery is an essential tool used for both diagnosis and treatment in many fields of medical applications. But, it takes specially trained medical specialists to read medical images and make diagnoses or treatment decisions. New practices of interpreting medical images are labour exhaustive, time-wasting, expensive, and prone to error. Using a computer-aided program which can render diagnosis and treatment decisions automatically would be more beneficial. A new computer-based detection method for the classification between compassionate and malignant mass tumours in mammography images of the breast proposed. (a) We planned to determine how to use the challenging definition, which produces severe examples that boost the segmentation of mammograms. (b) Employing well designing multi-instance learning through deep learning, we validated employing inadequately labelled data of breast cancer diagnosis using a mammogram. (c) The study is going through the Deep Lung method incorporating deep multi-dimensional automated identification and classification of the lung nodule. (d) By combining a probabilistic graphic model in deep learning, it authorizes how weakly labelled data can be used to improve the existing breast cancer identification method. This automated system involves manually defining the Region Of Interest (ROI), with the region and threshold values based on the next region. The High-Resolution Multi-View Deep Convolutional Neural Network (HRMP-DCNN) mainly developed for the extraction of function. The findings collected through the subsequent in available public databases like mammography screening information database and DDSM Curated Breast Imaging Subset. Ultimately, we’ll show the VGG that’s thousands of times quicker, and it is more reliable than earlier programmed anatomy segmentation.

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