An Artificial Neural Network Approach and a Data Augmentation Algorithm to Systematize the Diagnosis of Deep-Vein Thrombosis by Using Wells’ Criteria

Fong-Mata, María Berenice; García-Guerrero, E. E.; Mejía-Medina, David Abdel; López-Bonilla, Oscar Roberto; Villarreal-Gómez, Luis Jesús; Zamora-Arellano, Francisco; López-Mancilla, D.; Inzunza-González, Everardo

doi:10.3390/electronics9111810

Cited by 8 publications

(14 citation statements)

References 92 publications

(176 reference statements)

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“…ML has previously been studied for a variety of diagnostic approaches [19][20][21] . Several studies have applied ML in the context of VTE, although these ML applications have focused on developing CDSS that aid clinicians in VTE risk stratification of patients rather than diagnose VTE 12,22 . To the best of our knowledge, our work is a pioneering study that shows the potential benefits of ML for the diagnosis of DVT through imaging.…”

Section: Discussionmentioning

confidence: 99%

“…ML technology has previously been explored in the context of VTE, with several studies having shown the potential for ML clinical decision support systems (CDSS) to add incremental value in improving VTE risk stratification of patients. Most of these proposed CDSS are predominantly based upon the Wells criteria 12 , whilst others are more complex, taking into consideration a broader range of clinical risk factors for VTE as identified in the Caprini model (35 discrete clinical risk factors) 13 , 14 . However, to the best of our knowledge, no prior study has shown the potential benefit of ML to aid in the image-based diagnosis of DVT using ultrasound.…”

Section: Introductionmentioning

confidence: 99%

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Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning

Kainz

Heinrich

Makropoulos³

et al. 2021

npj Digit. Med.

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Deep vein thrombosis (DVT) is a blood clot most commonly found in the leg, which can lead to fatal pulmonary embolism (PE). Compression ultrasound of the legs is the diagnostic gold standard, leading to a definitive diagnosis. However, many patients with possible symptoms are not found to have a DVT, resulting in long referral waiting times for patients and a large clinical burden for specialists. Thus, diagnosis at the point of care by non-specialists is desired. We collect images in a pre-clinical study and investigate a deep learning approach for the automatic interpretation of compression ultrasound images. Our method provides guidance for free-hand ultrasound and aids non-specialists in detecting DVT. We train a deep learning algorithm on ultrasound videos from 255 volunteers and evaluate on a sample size of 53 prospectively enrolled patients from an NHS DVT diagnostic clinic and 30 prospectively enrolled patients from a German DVT clinic. Algorithmic DVT diagnosis performance results in a sensitivity within a 95% CI range of (0.82, 0.94), specificity of (0.70, 0.82), a positive predictive value of (0.65, 0.89), and a negative predictive value of (0.99, 1.00) when compared to the clinical gold standard. To assess the potential benefits of this technology in healthcare we evaluate the entire clinical DVT decision algorithm and provide cost analysis when integrating our approach into diagnostic pathways for DVT. Our approach is estimated to generate a positive net monetary benefit at costs up to £72 to £175 per software-supported examination, assuming a willingness to pay of £20,000/QALY.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning

Kainz

Heinrich

Makropoulos³

et al. 2021

npj Digit. Med.

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“…All models have been trained with

folds in the cross-validation search for model parameters. The value has been selected as a trade-off between the computational complexity (low number of folds leads to faster model selection) and prediction error (high number of folds leads to low prediction error) as discussed in [ 38 , 39 ]. Since the cross validation in this case is only used for primary selection of the model to be trained, the prediction error is not critical and the relatively low number of folds gives satisfactory results.…”

Section: One-dimensional Positioningmentioning

confidence: 99%

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Polák

Rozum

Slanina

et al. 2021

Sensors

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The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.

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“…The prevalence of this condition varies with age. It can cause DVT or pulmonary embolism (PE) in some cases [1,[8][9][10]; thrombosis can also develop in other veins such as the liver, cerebral sinus, retina, and mesenteric veins. Approximately one-third of VTE patients develop a PE, while two-thirds exclusively have DVT [11].…”

Section: Introductionmentioning

confidence: 99%

“…For the reasons stated above, the goal of this research is to propose several ML models that are trained by using a dataset of patients with the condition. It is collected from the state of the art [10] to have good judgment and clinical analysis to determine the diagnosis of DVT in a patient with the symptomatology of the condition, with the purpose of having a timely response and thus saving many lifes. In this research, the well-known Raspberry Pi 4 (RPi4) is employed as the edge-computing device to develop ML models and assess their performance in diagnosing DVT.…”

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

Self Cite

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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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An Artificial Neural Network Approach and a Data Augmentation Algorithm to Systematize the Diagnosis of Deep-Vein Thrombosis by Using Wells’ Criteria

Cited by 8 publications

References 92 publications

Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning

Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

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

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