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Chen Z, Wang Y, Ying MTC, Su Z. Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease. J Nephrol 2024:10.1007/s40620-023-01878-4. [PMID: 38315278 DOI: 10.1007/s40620-023-01878-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 12/26/2023] [Indexed: 02/07/2024]
Abstract
BACKGROUND Non-invasive renal fibrosis assessment is critical for tailoring personalized decision-making and managing follow-up in patients with chronic kidney disease (CKD). We aimed to exploit machine learning algorithms using clinical and elastosonographic features to distinguish moderate-severe fibrosis from mild fibrosis among CKD patients. METHODS A total of 162 patients with CKD who underwent shear wave elastography examinations and renal biopsies at our institution were prospectively enrolled. Four classifiers using machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and K-Nearest Neighbor (KNN), which integrated elastosonographic features and clinical characteristics, were established to differentiate moderate-severe renal fibrosis from mild forms. The area under the receiver operating characteristic curve (AUC) and average precision were employed to compare the performance of constructed models, and the SHapley Additive exPlanations (SHAP) strategy was used to visualize and interpret the model output. RESULTS The XGBoost model outperformed the other developed machine learning models, demonstrating optimal diagnostic performance in both the primary (AUC = 0.97, 95% confidence level (CI) 0.94-0.99; average precision = 0.97, 95% CI 0.97-0.98) and five-fold cross-validation (AUC = 0.85, 95% CI 0.73-0.98; average precision = 0.90, 95% CI 0.86-0.93) datasets. The SHAP approach provided visual interpretation for XGBoost, highlighting the features' impact on the diagnostic process, wherein the estimated glomerular filtration rate provided the largest contribution to the model output, followed by the elastic modulus, then renal length, renal resistive index, and hypertension. CONCLUSION This study proposed an XGBoost model for distinguishing moderate-severe renal fibrosis from mild forms in CKD patients, which could be used to assist clinicians in decision-making and follow-up strategies. Moreover, the SHAP algorithm makes it feasible to visualize and interpret the feature processing and diagnostic processes of the model output.
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Affiliation(s)
- Ziman Chen
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
| | - Yingli Wang
- Ultrasound Department, EDAN Instruments, Inc., Shenzhen, China
| | - Michael Tin Cheung Ying
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
| | - Zhongzhen Su
- Department of Ultrasound, Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
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Zhao H, Tang T, Lu Y, Li X, Sun L, Chen S, Ma L, Luo Y, Wang K, Zhao M. Development and Validation of Data-Level Innovation Data-Balancing Machine Learning Models for Predicting Optimal Implantable Collamer Lens Size and Postoperative Vault. Ophthalmol Ther 2024; 13:267-286. [PMID: 37943481 PMCID: PMC10776515 DOI: 10.1007/s40123-023-00841-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 10/20/2023] [Indexed: 11/10/2023] Open
Abstract
INTRODUCTION There are only four sizes of implantable collamer lens (ICL) available for selection, which cannot completely fit all patients as a result of the discontinuity of ICL sizes. Sizing an optimal ICL and predicting postoperative vault are still unresolved problems. This study aimed to develop and validate innovative data-level data-balancing machine learning-based models for predicting ICL size and postoperative vault. METHODS The patients were randomly assigned to training and internal validation sets in a 4:1 ratio. Feature selection was performed using analysis of variance (ANOVA) and Kruskal-Wallis feature importance methods. Traditional linear regression model and machine learning-based models were used. The accuracy of models was assessed using the area under the curve (AUC) and confusion matrix. RESULTS A total of 564 patients (1127 eyes) were eligible for this study, consisting of 808 eyes in the training set, 202 eyes in the internal validation set, and 117 eyes in the external validation set. Compared with the traditional linear regression method, the machine learning model bagging tree showed the best performance for ICL size selection, with an accuracy of 84.5% (95% confidence interval (CI) 83.2-85.8%), and the AUC ranged from 0.88 to 0.99; the prediction accuracy of 12.1 mm and 13.7 mm ICL sizes was improved by 49% and 59%, respectively. The bagging tree model achieved the best accuracy [90.2%, (95% CI 88.9-91.5%)] for predicting the postoperative vault, and the AUC ranged from 0.90 to 0.94. The prediction accuracies of internal and external validation dataset for ICL sizing were 82.2% (95% CI 81.1-83.3%) and 82.1% (95% CI 81.1-83.1%), respectively. CONCLUSIONS The innovative data-level data balancing-based machine learning model can be used to predict ICL size and postoperative vault more accurately, which can assist surgeons in choosing optimal ICL size, thus reducing risks of postoperative complications and secondary surgery.
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Affiliation(s)
- Heng Zhao
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Department of Ophthalmology and Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China
- College of Optometry, Peking University Health Science Center, Beijing, China
- Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China
- Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China
| | - Tao Tang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Department of Ophthalmology and Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China
- College of Optometry, Peking University Health Science Center, Beijing, China
- Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China
- Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China
| | - Yuchang Lu
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Department of Ophthalmology and Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China
- College of Optometry, Peking University Health Science Center, Beijing, China
- Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China
- Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China
| | - Xuewei Li
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Department of Ophthalmology and Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China
- College of Optometry, Peking University Health Science Center, Beijing, China
- Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China
- Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China
| | - Liyuan Sun
- Xuanwu Hospital Capital Medical University, Beijing, China
| | - Sitong Chen
- Department of Ophthalmology and Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China
- College of Optometry, Peking University Health Science Center, Beijing, China
- Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China
- Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China
| | - Lu Ma
- Department of Ophthalmology and Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China
- College of Optometry, Peking University Health Science Center, Beijing, China
- Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China
- Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China
| | - Yan Luo
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
| | - Kai Wang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China.
- Department of Ophthalmology and Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China.
- College of Optometry, Peking University Health Science Center, Beijing, China.
- Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China.
- Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China.
| | - Mingwei Zhao
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Department of Ophthalmology and Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China
- College of Optometry, Peking University Health Science Center, Beijing, China
- Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China
- Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China
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Fu R, Yu Z, Zhou C, Zhang J, Gao F, Wang D, Hao X, Pang X, Yu J. Artificial intelligence-based model for dose prediction of sertraline in adolescents: a real-world study. Expert Rev Clin Pharmacol 2024; 17:177-187. [PMID: 38197873 DOI: 10.1080/17512433.2024.2304009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 01/08/2024] [Indexed: 01/11/2024]
Abstract
BACKGROUND Variability exists in sertraline pharmacokinetic parameters in individuals, especially obvious in adolescents. We aimed to establish an individualized dosing model of sertraline for adolescents with depression based on artificial intelligence (AI) techniques. METHODS Data were collected from 258 adolescent patients treated at the First Hospital of Hebei Medical University between December 2019 to July 2022. Nine different algorithms were used for modeling to compare the prediction abilities on sertraline daily dose, including XGBoost, LGBM, CatBoost, GBDT, SVM, ANN, TabNet, KNN, and DT. Performance of four dose subgroups (50 mg, 100 mg, 150 mg, and 200 mg) were analyzed. RESULTS CatBoost was chosen to establish the individualized medication model with the best performance. Six important variables were found to be correlated with sertraline dose, including plasma concentration, PLT, MPV, GL, A/G, and LDH. The ROC curve and confusion matrix exhibited the good prediction performance of CatBoost model in four dose subgroups (the AUC of 50 mg, 100 mg, 150 mg, and 200 mg were 0.93, 0.81, 0.93, and 0.93, respectively). CONCLUSION The AI-based dose prediction model of sertraline in adolescents with depression had a good prediction ability, which provides guidance for clinicians to propose the optimal medication regimen.
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Affiliation(s)
- Ran Fu
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Ze Yu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Beijing Medicinovo Technology Co., Ltd, Beijing, China
| | - Chunhua Zhou
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jinyuan Zhang
- Beijing Medicinovo Technology Co., Ltd, Beijing, China
| | - Fei Gao
- Beijing Medicinovo Technology Co., Ltd, Beijing, China
| | - Donghan Wang
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xin Hao
- Dalian Medicinovo Technology Co., Ltd, Dalian, China
| | - Xiaolu Pang
- Department of Physical Diagnostics, Hebei Medical University, Shijiazhuang, China
| | - Jing Yu
- Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
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Blain Y, Alessandrino F, Scortegagna E, Balcacer P. Transplant renal artery stenosis: utilization of machine learning to identify ancillary sonographic and doppler parameters to predict stenosis in patients with graft dysfunction. Abdom Radiol (NY) 2023; 48:2102-2110. [PMID: 36947204 DOI: 10.1007/s00261-023-03872-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/23/2023]
Abstract
PURPOSE To determine if ancillary sonographic and Doppler parameters can be used to predict transplant renal artery stenosis in patients with renal graft dysfunction. MATERIALS AND METHODS IRB-approved, HIPAA-compliant retrospective study included 80 renal transplant patients who had renal US followed by renal angiogram between January 2018 and December 2019. A consensus read of two radiologists recorded these parameters: peak systolic velocity, persistence of elevated velocity, grayscale narrowing, parvus tardus, delayed systolic upstroke, angle of the systolic peak (SP angle), and aliasing. Univariate analysis using t-test or chi-square was performed to determine differences between patients with and without stenosis. P values under 0.05 were deemed statistically significant. We used machine learning algorithms to determine parameters that could better predict the presence of stenosis. The algorithms included logistic regression, random forest, imbalanced random forest, boosting, and CART. All 80 cases were split between training and testing using stratified sampling using a 75:25 split. RESULTS We found a statistically significant difference in grayscale narrowing (p = 0.0010), delayed systolic upstroke (p = 0.0002), SP angle (p = 0.0005), and aliasing (p = 0.0024) between the two groups. No significant difference was found for an elevated peak systolic velocity (p = 0.1684). The imbalanced random forest (IRF) model was selected for improved accuracy, sensitivity, and specificity. Specificity, sensitivity, AUC, and normalized Brier score for the IRF model using all parameters were 73%, 81%, 0.82, and 69 in the training set, and 78%, 58%, 0.78, and 80 in the testing set. VIMP assessment showed that the combination of variables that resulted in the most significant change of the training set performance was that of grayscale narrowing and SP angle. CONCLUSION Elevated peak systolic velocity did not discriminate between patients with and without TRAS. Adding ancillary parameters into the machine learning algorithm improved specificity and sensitivity similarly in the training and testing sets. The algorithm identified the combination of lumen narrowing coupled with the angle of the systolic peak as better predictor of TRAS. This model may improve the accuracy of ultrasound for transplant renal artery stenosis.
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Affiliation(s)
- Yamile Blain
- Department of Radiology, University of Miami Health System, 1611 NW 12th Ave, West Wing 279, Miami, FL, 33136, USA.
| | - Francesco Alessandrino
- Department of Radiology, University of Miami Health System, 1611 NW 12th Ave, West Wing 279, Miami, FL, 33136, USA
| | - Eduardo Scortegagna
- Department of Radiology, University of Miami Health System, 1611 NW 12th Ave, West Wing 279, Miami, FL, 33136, USA
| | - Patricia Balcacer
- Department of Radiology, University of Miami Health System, 1611 NW 12th Ave, West Wing 279, Miami, FL, 33136, USA
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Rasool DA, Ismail HJ, Yaba SP. Fully automatic carotid arterial stiffness assessment from ultrasound videos based on machine learning. Phys Eng Sci Med 2023; 46:151-164. [PMID: 36787022 DOI: 10.1007/s13246-022-01206-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 12/01/2022] [Indexed: 02/15/2023]
Abstract
Arterial stiffness (AS) refers to the loss of arterial compliance and alterations in vessel wall properties. The study of local carotid stiffness (CS) is particularly important since carotid artery stiffening raises the risk of stroke, cognitive impairment, and dementia. So, stiffness measurement as a screening tool approach is crucial because it can reduce mortality and facilitate therapy planning. This study aims to evaluate the stiffness of the CCA using machine learning (ML) through the features of diameter change (ΔD) and stiffness parameters. This study was conducted in seven stages: data collection, preprocessing, CCA segmentation, CCA lumen diameter (DCCA) computing during cardiac cycles, denoising signals of DCCA, computational of AS parameters, and stiffness assessment using ML. The 51 videos (with 25 s) of CCA B-mode ultrasound (US) were used and analyzed. Each US video yielded approximately 750 sequential frames spanning about 24 cardiac cycles. Firstly, US preset settings with time gain compensation with a U-pattern were employed to enhance CCA segmentations. The study showed that auto region-growing, employed three times, is appropriate for segmenting walls with a short running time (4.56 s/frame). The diameter computed for frames constructs a signal (diameter signal) with noisy parts in the shape of peak variance and an un-smooth side. Among the 12 employed smoothing methods, spline fitting with a mean peak difference per cycle (MPDCY) of 0.58 pixels was the most effective for the diameter signal. The authors propose the MPDCY as a new selection criterion for smoothing methods with highly preserved peaks. The ΔD (Dsys-Ddia) determined in this study was validated by statistical analysis as a viable replacement for manual ΔD measurement. Statistical analysis was carried out by Mann-Whitney t-test with a p-value of 0.81, regression line R2 = 0.907, and there was no difference in means between the two groups for box plots. The stiffness parameters of the carotid arteries were calculated based on auto-ΔD and pulse pressure. Five ML models, including K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and random forest (RF), fed by distension (ΔD) and five stiffness parameters, were used to distinguish between the stiffened and un-stiffened CCA. Except for SVM, all models performed excellently in terms of specificity, sensitivity, precision, and area under the curve (AUC). In addition, the scatterplot and statistical analysis of the fed features confirm these remarkable outcomes. The scatter plot demonstrates that a linear hyperline can easily distinguish between the two classes. The statistical analysis shows that the stiffness parameters computed from the database of this work were statistically (p < 0.05) distributed into the non-stiffness and stiffness groups. The presented models are validated by applying them to additional datasets. Applying models to other datasets reveals a model performance of 100%. The proposed ML models could be applied in clinical practice to detect CS early, which is essential for preventing stroke.
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