Comparison of the Accuracy of Emergency Department–Performed Point-of-Care Ultrasound in the Diagnosis of Lower-Extremity Deep Vein Thrombosis

Ly-Pen, Domingo; Penedo-Alonso, Roberto; Sánchez-Pérez, Mario

doi:10.1016/j.jemermed.2018.06.042

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…On the other hand, it is well known that ultrasound is the standard test for the diagnosis of DVT and that it is one of the most accurate, and recently, they are also using ML techniques for the diagnosis of DVT [19]. However, the accuracy of the examinations improves with experience and the training that a sonographer gains in their working life, so the percentage is not always the same and is not very high at first [20,21]. Although there is research [22] that strives to combine Deep Learning (DL) and magnetic resonance imaging, with promising outcomes.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis

Contreras-Luján

García-Guerrero

López-Bonilla

et al. 2022

MCA

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Deep venous thrombosis (DVT) is a disease that must be diagnosed quickly, as it can trigger the death of patients. Nowadays, one can find different ways to determine it, including clinical scoring, D-dimer, ultrasonography, etc. Recently, scientists have focused efforts on using machine learning (ML) and neural networks for disease diagnosis, progressively increasing the accuracy and efficacy. Patients with suspected DVT have no apparent symptoms. Using pattern recognition techniques, aiding good timely diagnosis, as well as well-trained ML models help to make good decisions and validation. The aim of this paper is to propose several ML models for a more efficient and reliable DVT diagnosis through its implementation on an edge device for the development of instruments that are smart, portable, reliable, and cost-effective. The dataset was obtained from a state-of-the-art article. It is divided into 85% for training and cross-validation and 15% for testing. The input data in this study are the Wells criteria, the patient’s age, and the patient’s gender. The output data correspond to the patient’s diagnosis. This study includes the evaluation of several classifiers such as Decision Trees (DT), Extra Trees (ET), K-Nearest Neighbor (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Random Forest (RF), and Support Vector Machine (SVM). Finally, the implementation of these ML models on a high-performance embedded system is proposed to develop an intelligent system for early DVT diagnosis. It is reliable, portable, open source, and low cost. The performance of different ML algorithms was evaluated, where KNN achieved the highest accuracy of 90.4% and specificity of 80.66% implemented on personal computer (PC) and Raspberry Pi 4 (RPi4). The accuracy of all trained models on PC and Raspberry Pi 4 is greater than 85%, while the area under the curve (AUC) values are between 0.81 and 0.86. In conclusion, as compared to traditional methods, the best ML classifiers are effective at predicting DVT in an early and efficient manner.

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