Developing an iOS application that uses machine learning for the automated diagnosis of blepharoptosis

2021 ◽  
Author(s):  
Hitoshi Tabuchi ◽  
Daisuke Nagasato ◽  
Hiroki Masumoto ◽  
Mao Tanabe ◽  
Naofumi Ishitobi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automated Diagnosis ◽  
Ios Application

scholarly journals Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning

PLoS ONE ◽  
2019 ◽  
Vol 14 (9) ◽  
pp. e0222983 ◽  
Author(s):  
Bernhard Vennemann ◽  
Dominik Obrist ◽  
Thomas Rösgen
Keyword(s):  
Machine Learning ◽  
Heart Valve ◽  
Novelty Detection ◽  
Automated Diagnosis ◽  
Detection Algorithms

Machine-learning neuroimaging challenge for automated diagnosis of mild cognitive impairment: Lessons learnt

2018 ◽  
Vol 302 ◽  
pp. 10-13 ◽  
Author(s):  
Isabella Castiglioni ◽  
Christian Salvatore ◽  
Javier Ramírez ◽  
Juan Manuel Górriz
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Automated Diagnosis ◽  
Lessons Learnt

scholarly journals Automated histologic diagnosis of CNS tumors with machine learning

CNS Oncology ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. CNS56
Author(s):  
Siri Sahib S Khalsa ◽  
Todd C Hollon ◽  
Arjun Adapa ◽  
Esteban Urias ◽  
Sudharsan Srinivasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Impact ◽  
Machine Learning Algorithms ◽  
Small Specimen ◽  
Automated Diagnosis ◽  
Histologic Diagnosis ◽  
Future Studies ◽  
Resource Poor ◽  
Automated Interpretation ◽  
The Brain

The discovery of a new mass involving the brain or spine typically prompts referral to a neurosurgeon to consider biopsy or surgical resection. Intraoperative decision-making depends significantly on the histologic diagnosis, which is often established when a small specimen is sent for immediate interpretation by a neuropathologist. Access to neuropathologists may be limited in resource-poor settings, which has prompted several groups to develop machine learning algorithms for automated interpretation. Most attempts have focused on fixed histopathology specimens, which do not apply in the intraoperative setting. The greatest potential for clinical impact probably lies in the automated diagnosis of intraoperative specimens. Successful future studies may use machine learning to automatically classify whole-slide intraoperative specimens among a wide array of potential diagnoses.

scholarly journals A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-Ray Images

2021 ◽  
Vol 66 (3) ◽  
pp. 3289-3310
Author(s):  
Mazin Abed Mohammed ◽  
Karrar Hameed Abdulkareem ◽  
Begonya Garcia-Zapirain ◽  
Salama A. Mostafa ◽  
Mashael S. Maashi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Classification Methods ◽  
Automated Diagnosis ◽  
Comprehensive Investigation ◽  
X Ray

Performance analysis of machine learning algorithms for automated diagnosis using a dataflow-based approach on the grid

2015 ◽  
Vol 1 (2/3) ◽  
pp. 261 ◽  
Author(s):  
Frederico Valente ◽  
Augusto Silva ◽  
Carlos Manuel Azevedo Costa ◽  
José Miguel Franco Valiente ◽  
César Suárez Ortega
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Automated Diagnosis
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