Early detection of ankylosing spondylitis using texture features and statistical machine learning, and deep learning, with some patient age analysis

Castro-Zunti, Riel; Park, Eun Hae; Choi, Younhee; Jin, Gong Yong; Ko, Seok‐Bum

doi:10.1016/j.compmedimag.2020.101718

Cited by 30 publications

(15 citation statements)

References 18 publications

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“…Machine learning is widely used in diagnosing, treating, treating, preventing, and managing AS diseases. Riel et al used computed tomography (CT) to construct an early diagnosis model using machine learning methods [ 47 ]. Samuel et al used single-cell transcriptome and surface epitope analysis of AS to classify diseases using machine learning methods [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis

Zhu

Liang

et al. 2022

Rheumatol Ther

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Introduction: Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS mainly affects the axial bone, sacroiliac joint, hip joint, spinal facet, and adjacent ligaments. We used machine learning (ML) methods to construct diagnostic models based on blood routine examination, liver function test, and kidney function test of patients with AS. This method will help clinicians enhance diagnostic efficiency and allow patients to receive systematic treatment as soon as possible.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis

Zhu

Liang

et al. 2022

Rheumatol Ther

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“…It can be used for early diagnosis of AS by creating a machine learning model with image data as well as text data, because images such as radiographs are important in the diagnosis of AS. The detection of sacroiliitis using X-ray, computer tomography, and magnetic resonance images using machine learning methods has been conducted in recent years showing excellent performance in screening of patients with AS [6,8,33]. Therefore, developing a machine learning model useful for diagnosis by combining image, life-log, and clinical information is essential to improve diagnosis accuracy, which is worthy of future challenges for the prediction of radiographic progression in patients with AS.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Models with Time-Series Clinical Features to Predict Radiographic Progression in Patients with Ankylosing Spondylitis

Koo

Jang

et al. 2022

SSRN Journal

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“…KNN classifies a data point based on its distance from the maximum number of training data points in the neighborhood. Typically, KNN uses Euclidean, Minkowski, Manhattan, or Hamming distances out of which Minkowski distance has been reported to be more reliable [ 48 ] and was therefore selected in the model. LR classifies data points into discrete classes based on probability using a sigmoid or logistic function [ 49 ].…”

Section: Methodsmentioning

confidence: 99%

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Chandel

Rajwade

Dubey

et al. 2022

Plants

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Timely crop water stress detection can help precision irrigation management and minimize yield loss. A two-year study was conducted on non-invasive winter wheat water stress monitoring using state-of-the-art computer vision and thermal-RGB imagery inputs. Field treatment plots were irrigated using two irrigation systems (flood and sprinkler) at four rates (100, 75, 50, and 25% of crop evapotranspiration [ETc]). A total of 3200 images under different treatments were captured at critical growth stages, that is, 20, 35, 70, 95, and 108 days after sowing using a custom-developed thermal-RGB imaging system. Crop and soil response measurements of canopy temperature (Tc), relative water content (RWC), soil moisture content (SMC), and relative humidity (RH) were significantly affected by the irrigation treatments showing the lowest Tc (22.5 ± 2 °C), and highest RWC (90%) and SMC (25.7 ± 2.2%) for 100% ETc, and highest Tc (28 ± 3 °C), and lowest RWC (74%) and SMC (20.5 ± 3.1%) for 25% ETc. The RGB and thermal imagery were then used as inputs to feature-extraction-based deep learning models (AlexNet, GoogLeNet, Inception V3, MobileNet V2, ResNet50) while, RWC, SMC, Tc, and RH were the inputs to function-approximation models (Artificial Neural Network (ANN), Kernel Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM) and Long Short-Term Memory (DL-LSTM)) to classify stressed/non-stressed crops. Among the feature extraction-based models, ResNet50 outperformed other models showing a discriminant accuracy of 96.9% with RGB and 98.4% with thermal imagery inputs. Overall, classification accuracy was higher for thermal imagery compared to RGB imagery inputs. The DL-LSTM had the highest discriminant accuracy of 96.7% and less error among the function approximation-based models for classifying stress/non-stress. The study suggests that computer vision coupled with thermal-RGB imagery can be instrumental in high-throughput mitigation and management of crop water stress.

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Early detection of ankylosing spondylitis using texture features and statistical machine learning, and deep learning, with some patient age analysis

Cited by 30 publications

References 18 publications

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis

Machine Learning Models with Time-Series Clinical Features to Predict Radiographic Progression in Patients with Ankylosing Spondylitis

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

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