CT-Angiography-Based Outcome Prediction on Diabetic Foot Ulcer Patients: A Statistical Learning Approach
The purpose of our study is to predict the occurrence and prognosis of diabetic foot ulcers (DFUs) by clinical and lower extremity computed tomography angiography (CTA) data of patients using the artificial neural networks (ANN) model. DFU is a common complication of diabetes that severely affects t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/5/1076 |
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| author | Di Zhang Wei Dong Haonan Guan Aobuliaximu Yakupu Hanqi Wang Liuping Chen Shuliang Lu Jiajun Tang |
| author_facet | Di Zhang Wei Dong Haonan Guan Aobuliaximu Yakupu Hanqi Wang Liuping Chen Shuliang Lu Jiajun Tang |
| author_sort | Di Zhang |
| collection | DOAJ |
| description | The purpose of our study is to predict the occurrence and prognosis of diabetic foot ulcers (DFUs) by clinical and lower extremity computed tomography angiography (CTA) data of patients using the artificial neural networks (ANN) model. DFU is a common complication of diabetes that severely affects the quality of life of patients, leading to amputation and even death. There are a lack of valid predictive techniques for the prognosis of DFU. In clinical practice, the use of scales alone has a large subjective component, leading to significant bias and heterogeneity. Currently, there is a lack of evidence-based support for patients to develop clinical strategies before reaching end-stage outcomes. The present study provides a novel technical tool for predicting the prognosis of DFU. After screening the data, 203 patients with diabetic foot ulcers (DFUs) were analyzed and divided into two subgroups based on their Wagner Score (138 patients in the low Wagner Score group and 65 patients in the high Wagner Score group). Based on clinical and lower extremity CTA data, 10 predictive factors were selected for inclusion in the model. The total dataset was randomly divided into the training sample, testing sample and holdout sample in ratio of 3:1:1. After the training sample and testing sample developing the ANN model, the holdout sample was utilized to assess the accuracy of the model. ANN model analysis shows that the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) of the overall ANN model were 92.3%, 93.5%, 87.0%, 94.2% and 0.955, respectively. We observed that the proposed model performed superbly on the prediction of DFU with a 91.6% accuracy. Evaluated with the holdout sample, the model accuracy, sensitivity, specificity, PPV and NPV were 88.9%, 90.0%, 88.5%, 75.0% and 95.8%, respectively. By contrast, the logistic regression model was inferior to the ANN model. The ANN model can accurately and reliably predict the occurrence and prognosis of a DFU according to clinical and lower extremity CTA data. We provided clinicians with a novel technical tool to develop clinical strategies before end-stage outcomes. |
| first_indexed | 2024-03-10T03:02:31Z |
| format | Article |
| id | doaj.art-0aa9680022be47c59b465eb7923f302e |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T03:02:31Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-0aa9680022be47c59b465eb7923f302e2023-11-23T10:39:02ZengMDPI AGDiagnostics2075-44182022-04-01125107610.3390/diagnostics12051076CT-Angiography-Based Outcome Prediction on Diabetic Foot Ulcer Patients: A Statistical Learning ApproachDi Zhang0Wei Dong1Haonan Guan2Aobuliaximu Yakupu3Hanqi Wang4Liuping Chen5Shuliang Lu6Jiajun Tang7Department of Burn, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaWound Healing Department, Shanghai YongCi Rehabilitation Hospital, Shanghai 200025, ChinaDepartment of Burn, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaDepartment of Burn, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaDepartment of Burn, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaDepartment of Burn, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaThe purpose of our study is to predict the occurrence and prognosis of diabetic foot ulcers (DFUs) by clinical and lower extremity computed tomography angiography (CTA) data of patients using the artificial neural networks (ANN) model. DFU is a common complication of diabetes that severely affects the quality of life of patients, leading to amputation and even death. There are a lack of valid predictive techniques for the prognosis of DFU. In clinical practice, the use of scales alone has a large subjective component, leading to significant bias and heterogeneity. Currently, there is a lack of evidence-based support for patients to develop clinical strategies before reaching end-stage outcomes. The present study provides a novel technical tool for predicting the prognosis of DFU. After screening the data, 203 patients with diabetic foot ulcers (DFUs) were analyzed and divided into two subgroups based on their Wagner Score (138 patients in the low Wagner Score group and 65 patients in the high Wagner Score group). Based on clinical and lower extremity CTA data, 10 predictive factors were selected for inclusion in the model. The total dataset was randomly divided into the training sample, testing sample and holdout sample in ratio of 3:1:1. After the training sample and testing sample developing the ANN model, the holdout sample was utilized to assess the accuracy of the model. ANN model analysis shows that the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) of the overall ANN model were 92.3%, 93.5%, 87.0%, 94.2% and 0.955, respectively. We observed that the proposed model performed superbly on the prediction of DFU with a 91.6% accuracy. Evaluated with the holdout sample, the model accuracy, sensitivity, specificity, PPV and NPV were 88.9%, 90.0%, 88.5%, 75.0% and 95.8%, respectively. By contrast, the logistic regression model was inferior to the ANN model. The ANN model can accurately and reliably predict the occurrence and prognosis of a DFU according to clinical and lower extremity CTA data. We provided clinicians with a novel technical tool to develop clinical strategies before end-stage outcomes.https://www.mdpi.com/2075-4418/12/5/1076diabetic foot ulcerartificial neural networkslower extremity CT angiography |
| spellingShingle | Di Zhang Wei Dong Haonan Guan Aobuliaximu Yakupu Hanqi Wang Liuping Chen Shuliang Lu Jiajun Tang CT-Angiography-Based Outcome Prediction on Diabetic Foot Ulcer Patients: A Statistical Learning Approach Diagnostics diabetic foot ulcer artificial neural networks lower extremity CT angiography |
| title | CT-Angiography-Based Outcome Prediction on Diabetic Foot Ulcer Patients: A Statistical Learning Approach |
| title_full | CT-Angiography-Based Outcome Prediction on Diabetic Foot Ulcer Patients: A Statistical Learning Approach |
| title_fullStr | CT-Angiography-Based Outcome Prediction on Diabetic Foot Ulcer Patients: A Statistical Learning Approach |
| title_full_unstemmed | CT-Angiography-Based Outcome Prediction on Diabetic Foot Ulcer Patients: A Statistical Learning Approach |
| title_short | CT-Angiography-Based Outcome Prediction on Diabetic Foot Ulcer Patients: A Statistical Learning Approach |
| title_sort | ct angiography based outcome prediction on diabetic foot ulcer patients a statistical learning approach |
| topic | diabetic foot ulcer artificial neural networks lower extremity CT angiography |
| url | https://www.mdpi.com/2075-4418/12/5/1076 |
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