scholarly journals Differential diagnosis of hereditary anemias from a fraction of blood drop by digital holography and hierarchical machine learning

2022 ◽  
pp. 113945
Author(s):  
Pasquale Memmolo ◽  
Genny Aprea ◽  
Vittorio Bianco ◽  
Roberta Russo ◽  
Immacolata Andolfo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Digital Holography

scholarly journals Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jung Su Lee ◽  
Jihye Yun ◽  
Sungwon Ham ◽  
Hyunjung Park ◽  
Hyunsu Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Previous History ◽  
Clinical Factor ◽  
Endoscopic Images ◽  
Simplex Virus

AbstractThe endoscopic features between herpes simplex virus (HSV) and cytomegalovirus (CMV) esophagitis overlap significantly, and hence the differential diagnosis between HSV and CMV esophagitis is sometimes difficult. Therefore, we developed a machine-learning-based classifier to discriminate between CMV and HSV esophagitis. We analyzed 87 patients with HSV esophagitis and 63 patients with CMV esophagitis and developed a machine-learning-based artificial intelligence (AI) system using a total of 666 endoscopic images with HSV esophagitis and 416 endoscopic images with CMV esophagitis. In the five repeated five-fold cross-validations based on the hue–saturation–brightness color model, logistic regression with a least absolute shrinkage and selection operation showed the best performance (sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating characteristic curve: 100%, 100%, 100%, 100%, 100%, and 1.0, respectively). Previous history of transplantation was included in classifiers as a clinical factor; the lower the performance of these classifiers, the greater the effect of including this clinical factor. Our machine-learning-based AI system for differential diagnosis between HSV and CMV esophagitis showed high accuracy, which could help clinicians with diagnoses.

scholarly journals Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

2019 ◽  
Vol 45 (10) ◽  
pp. 3193-3201 ◽  
Author(s):  
Yajuan Li ◽  
Xialing Huang ◽  
Yuwei Xia ◽  
Liling Long
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Cell Carcinoma ◽  
Area Under The Curve ◽  
Image Features ◽  
Renal Tumors ◽  
Support Vector ◽  
Svm Classifier ◽  
Renal Oncocytoma ◽  
Lasso Regression

Abstract Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CT-enhanced quantitative features and machine learning.

Machine learning reveals the most important psychological and social variables predicting the differential diagnosis of rheumatic and musculoskeletal diseases

2021 ◽  
Author(s):  
Germano Vera Cruz ◽  
Emilie Bucourt ◽  
Christian Réveillère ◽  
Virginie Martaillé ◽  
Isabelle Joncker-Vannier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Musculoskeletal Diseases ◽  
Social Variables

Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson’s disease using [123I]FP-CIT SPECT

2014 ◽  
Vol 42 (1) ◽  
pp. 112-119 ◽  
Author(s):  
I. Huertas-Fernández ◽  
F. J. García-Gómez ◽  
D. García-Solís ◽  
S. Benítez-Rivero ◽  
V. A. Marín-Oyaga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Differential Diagnosis ◽  
Learning Models ◽  
Vascular Parkinsonism ◽  
Machine Learning Models

Su1880 MACHINE LEARNING APPROACHES VALIDATE SEROLOGICAL MARKERS FOR EARLY AND DIFFERENTIAL DIAGNOSIS

2020 ◽  
Vol 158 (6) ◽  
pp. S-687
Author(s):  
Sare Verstockt ◽  
Nora Verplaetse ◽  
Daniele Raimondi ◽  
Bram Verstockt ◽  
Elien Glorieus ◽  
...  
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Learning Approaches ◽  
Serological Markers

scholarly journals BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiejie Zhou ◽  
Yan-Lin Liu ◽  
Yang Zhang ◽  
Jeon-Hor Chen ◽  
Freddie J. Combs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Regional Distribution ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mass Lesions ◽  
Morphological Distribution ◽  
Parametric Maps

BackgroundA wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions.Materials and MethodsA total of 150 patients with 104 malignant and 46 benign NME were analyzed. Three radiologists performed reading for morphological distribution and internal enhancement using the 5th BI-RADS lexicon. For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation. Three DCE parametric maps related to wash-in, maximum, and wash-out were generated, and PyRadiomics was applied to extract features. The radiomics model was built using five machine learning algorithms. ResNet50 was implemented using three parametric maps as input. Approximately 70% of earlier cases were used for training, and 30% of later cases were held out for testing.ResultsThe diagnostic BI-RADS in the original MRI report showed that 104/104 malignant and 36/46 benign lesions had a BI-RADS score of 4A–5. For category reading, the kappa coefficient was 0.83 for morphological distribution (excellent) and 0.52 for internal enhancement (moderate). Segmental and Regional distribution were the most prominent for the malignant group, and focal distribution for the benign group. Eight radiomics features were selected by support vector machine (SVM). Among the five machine learning algorithms, SVM yielded the highest accuracy of 80.4% in training and 77.5% in testing datasets. ResNet50 had a better diagnostic performance, 91.5% in training and 83.3% in testing datasets.ConclusionDiagnosis of NME was challenging, and the BI-RADS scores and descriptors showed a substantial overlap. Radiomics and deep learning may provide a useful CAD tool to aid in diagnosis.

scholarly journals Differential Diagnosis of Tuberculosis and Pneumonia using Machine Learning

2019 ◽  
Vol 8 (11) ◽  
pp. 245-250
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Differential Diagnosis ◽  
Medical Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Care Practice ◽  
Digital Revolution ◽  
Learning Techniques ◽  
Classification Prediction

Machine learning has become one of the top most emerging technologies in this era of digital revolution. The machine learning algorithms are being used in various fields and applications such as image recognition, speech recognition, classification, prediction, medical diagnosis etc. In medical domain, machine learning techniques have been successfully implemented to improve the accuracy of medical diagnosis and also to improve the efficiency and quality of health care. In this paper, we have analyzed the existing health care practice system and have proposed how machine learning techniques can be used for differential diagnosis of Tuberculosis and Pneumonia which are often misdiagnosed due to similar symptoms at early stages.

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