AIMC Topic: Diagnosis, Differential

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Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study.

European radiology Jul 1, 2019
OBJECTIVES: To evaluate the diagnostic performance of deep learning with the convolutional neural networks (CNN) to distinguish each representative parkinsonian disorder using MRI.
Aged, 80 and over Adult Male Middle Aged Neural Networks, Computer Retrospective Studies Female Aged Diagnosis, Differential Parkinsonian Disorders Proof of Concept Study Multiple System Atrophy Supranuclear Palsy, Progressive Deep Learning ROC Curve Magnetic Resonance Imaging Parkinson Disease Humans
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Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis.

Journal of clinical pathology Jun 29, 2019
AIMS: Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagn...
Blood Specimen Collection Cell Lineage Leukemia Humans Machine Learning Acute Disease Leukocytes Image Interpretation, Computer-Assisted Pattern Recognition, Automated Reproducibility of Results Staining and Labeling Diagnosis, Differential Predictive Value of Tests
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F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma.

NeuroImage. Clinical Jun 27, 2019
The differential diagnosis of primary central nervous system lymphoma from glioblastoma multiforme (GBM) is essential due to the difference in treatment strategies. This study retrospectively reviewed 77 patients (24 with lymphoma and 53 with GBM) to...
Lymphoma Aged Humans Neuroimaging Brain Neoplasms Positron-Emission Tomography Machine Learning Diagnosis, Differential Male Retrospective Studies Glioblastoma Fluorodeoxyglucose F18 Middle Aged Female Radiopharmaceuticals
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Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model.

Gastroenterology Jun 25, 2019
BACKGROUND & AIMS: Capsule endoscopy has revolutionized investigation of the small bowel. However, this technique produces a video that is 8-10 hours long, so analysis is time consuming for gastroenterologists. Deep convolutional neural networks (CNN...
Observer Variation Capsule Endoscopy Intestine, Small Intestinal Diseases Clinical Competence Diagnosis, Differential Predictive Value of Tests Image Interpretation, Computer-Assisted Gastroenterologists Humans Reproducibility of Results Deep Learning China Prognosis Retrospective Studies
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Comparison of F-FDG avidity at PET of benign and malignant pure ground-glass opacities: a paradox? Part II: artificial neural network integration of the PET/CT characteristics of ground-glass opacities to predict their likelihood of malignancy.

Clinical radiology Jun 12, 2019
AIM: To assess the ability of artificial neural networks (ANNs) to predict the likelihood of malignancy of pure ground-glass opacities (GGOs), using observations from computed tomography (CT) and 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission...
Female Adult Male Lung Neoplasms Diagnosis, Differential Humans Neural Networks, Computer Retrospective Studies Predictive Value of Tests Fluorodeoxyglucose F18 Positron Emission Tomography Computed Tomography Radiographic Image Interpretation, Computer-Assisted Radiopharmaceuticals
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Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.

Medical physics Jun 7, 2019
PURPOSE: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from...
Female Adult Humans Male Machine Learning Middle Aged Diagnosis, Differential Image Processing, Computer-Assisted Tomography, X-Ray Computed Reference Standards Lung Neoplasms
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Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images.

Academic radiology May 27, 2019
RATIONALE AND OBJECTIVES: To evaluate the ability of artificial neural networks (ANN) fed with radiomic signatures (RSs) extracted from multidetector computed tomography images in differentiating the histopathological grades of clear cell renal cell ...
Multidetector Computed Tomography Carcinoma, Renal Cell Machine Learning Retrospective Studies Diagnosis, Differential Kidney Neoplasms Humans Neural Networks, Computer
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Diagnosis of cervical squamous cell carcinoma and cervical adenocarcinoma based on Raman spectroscopy and support vector machine.

Photodiagnosis and photodynamic therapy May 25, 2019
In this report, we collected the Raman spectrum of cervical adenocarcinoma and cervical squamous cell carcinoma tissues by a micro-Raman spectroscopy system. We analysed, compared and summarized the characteristics and differences of the normalized m...
Humans Female Carcinoma, Squamous Cell Diagnosis, Differential Adenocarcinoma Sensitivity and Specificity Support Vector Machine Principal Component Analysis Uterine Cervical Neoplasms Spectrum Analysis, Raman
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Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma.

Ultrasound in medicine & biology May 25, 2019
The question of whether ultrasound point shear wave elastography can differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML) is controversial. This study prospectively enrolled 51 patients with 52 renal tumors (42 RCCs, 10 AMLs). We obtain...
Sensitivity and Specificity Humans Aged Male Machine Learning Elasticity Imaging Techniques Image Interpretation, Computer-Assisted Aged, 80 and over Middle Aged Prospective Studies Kidney Neoplasms Adolescent Young Adult Angiomyolipoma Female Carcinoma, Renal Cell Adult Reproducibility of Results Kidney Diagnosis, Differential
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Diagnostic ability of artificial intelligence using deep learning analysis of cyst fluid in differentiating malignant from benign pancreatic cystic lesions.

Scientific reports May 3, 2019
The diagnosis of pancreatic cystic lesions remains challenging. This study aimed to investigate the diagnostic ability of carcinoembryonic antigen (CEA), cytology, and artificial intelligence (AI) by deep learning using cyst fluid in differentiating ...
Deep Learning Diagnosis, Differential Female Pancreatic Neoplasms Cyst Fluid Humans Aged Carcinoembryonic Antigen Amylases Male Pancreatic Cyst Middle Aged Retrospective Studies
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