Intelligent Diagnosis in Trauma: Exploring Machine Learning and Radiomics for Kidney Injury Assessment
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Abstract
Purpose: The initial evaluation of trauma poses a formidable and time-intensive challenge. This study aims to scrutinize the diagnostic efficacy and utility of integrating machine learning models with radiomics features for the identification of blunt traumatic kidney injuries in abdominal CT images.
Materials and Methods: This investigation involved the collection of 600 CT scan images encompassing individuals with varying degrees of kidney damage resulting from trauma, as well as images from healthy subjects, sourced from the Kaggle dataset. An experienced radiologist performed the segmentation of axial images, and radiomics features were subsequently extracted from each region of interest. Initially, 30 machine learning models were deployed, with a final selection narrowed down to three models: Light Gradient-Boosting Machine (LGBM), Ridge Classifier, and Adaptive Boosting (AdaBoost). The performance of these chosen models was subjected to a more comprehensive examination.
Results: The AdaBoost model exhibited notable performance in diagnosing mild kidney injury, achieving accuracy and sensitivity rates of 93% and 94%, respectively. Furthermore, for severe kidney injury, the AdaBoost model demonstrated a remarkable sensitivity of 96% and an accuracy of 97%. The Area Under the Curve (AUC) values for this model were also calculated, yielding values of 92.91% and 97.04% for mild and severe renal injuries, respectively.
Conclusion: The artificial intelligence models employed in this study hold significant potential to enhance patient care by providing valuable assistance to radiologists and other medical professionals in the diagnosis and staging of trauma-related kidney injuries. These models offer the capability to prioritize positive studies, expedite evaluations, and accurately identify more severe injuries that may necessitate immediate intervention.
1- Javad Salimi and MR NIKOUBAKHT, "Epidemiologic study of 284 patients with urogenital trauma in three trauma centers in Tehran." (2004).
2- M. McPhee, N. Arumainayagam, M. Clark, N. Burfitt, and R. DasGupta, "Renal injury management in an urban trauma centre and implications for urological training." (in eng), Ann R Coll Surg Engl, Vol. 97 (No. 3), pp. 194-7, Apr (2015).
3- M. T. Heller and N. Schnor, "MDCT of renal trauma: correlation to AAST organ injury scale." (in eng), Clin Imaging, Vol. 38 (No. 4), pp. 410-17, Jul-Aug (2014).
4- T. Ząbkowski, R. Skiba, M. Saracyn, and H. Zieliński, "Analysis of Renal Trauma in Adult Patients: A 6-Year Own Experiences of Trauma Center." (in eng), Urol J, Vol. 12 (No. 4), pp. 2276-9, Sep 4 (2015).
5- T. Erlich and N. D. Kitrey, "Renal trauma: the current best practice." (in eng), Ther Adv Urol, Vol. 10 (No. 10), pp. 295-303, Oct (2018).
6- B. B. Voelzke and L. Leddy, "The epidemiology of renal trauma." (in eng), Transl Androl Urol, Vol. 3 (No. 2), pp. 143-9, Jun (2014).
7- W. Szmigielski, R. Kumar, S. Al Hilli, and M. Ismail, "Renal trauma imaging: Diagnosis and management. A pictorial review." (in eng), Pol J Radiol, Vol. 78 (No. 4), pp. 27-35, Oct (2013).
8- Xiang Li et al., "Deep learning-enabled system for rapid pneumothorax screening on chest CT." European journal of radiology, Vol. 120p. 108692, (2019).
9- H. P. Chan, R. K. Samala, and L. M. Hadjiiski, "CAD and AI for breast cancer-recent development and challenges." (in eng), Br J Radiol, Vol. 93 (No. 1108), p. 20190580, Apr (2020).
10- S. Wang and R. M. Summers, "Machine learning and radiology." Med Image Anal, Vol. 16 (No. 5), pp. 933-51, Jul (2012).
11- Sheetal Chaudhuri et al., "Artificial intelligence enabled applications in kidney disease." in Seminars in dialysis, (2021), Vol. 34 (No. 1): Wiley Online Library, pp. 5-16.
12- Nafiseh Ghaffar Nia, Erkan Kaplanoglu, and Ahad Nasab, "Evaluation of artificial intelligence techniques in disease diagnosis and prediction." Discover Artificial Intelligence, Vol. 3 (No. 1), p. 5, 2023/01/30 (2023).
13- H. Alimiri Dehbaghi, K. Khoshgard, H. Sharini, and S. J. Khairabadi, "Diagnosis of traumatic liver injury on computed tomography using machine learning algorithms and radiomics features: The role of artificial intelligence for rapid diagnosis in emergency rooms." (in eng), J Res Med Sci, Vol. 29p. 77, (2024).
14- Robert J Gillies, Paul E Kinahan, and Hedvig Hricak, "Radiomics: images are more than pictures, they are data." Radiology, Vol. 278 (No. 2), pp. 563-77, (2016).
15- Marius E Mayerhoefer et al., "Introduction to radiomics." Journal of Nuclear Medicine, Vol. 61 (No. 4), pp. 488-95, (2020).
16- Ruimeng Yang et al., "Radiomics of small renal masses on multiphasic CT: accuracy of machine learning–based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat." European radiology, Vol. 30pp. 1254-63, (2020).
17- Philippe Lambin et al., "Radiomics: extracting more information from medical images using advanced feature analysis." European journal of cancer, Vol. 48 (No. 4), pp. 441-46, (2012).
18- Hossein Naseri et al., "Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest." Scientific Reports, Vol. 12 (No. 1), p. 9866, 2022/06/14 (2022).
19- Andrew Laine and Jian Fan, "Texture classification by wavelet packet signatures." IEEE Transactions on pattern analysis and machine intelligence, Vol. 15 (No. 11), pp. 1186-91, (1993).
20- Alex Zwanenburg et al., "The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping." Radiology, Vol. 295 (No. 2), pp. 328-38, (2020).
21- Guillaume Thibault, Jesus Angulo, and Fernand Meyer, "Advanced statistical matrices for texture characterization: application to cell classification." IEEE Transactions on Biomedical Engineering, Vol. 61 (No. 3), pp. 630-37, (2013).
22- Ethem Alpaydin, Introduction to machine learning. MIT press, (2020).
23- Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). (2009).
24- Baoyang Cui, Zhonglin Ye, Haixing Zhao, Zhuome Renqing, Lei Meng, and Yanlin Yang, "Used Car Price Prediction Based on the Iterative Framework of XGBoost+LightGBM." Electronics, Vol. 11 (No. 18), p. 2932, (2022).
25- H. Alimiri Dehbaghi, K. Khoshgard, H. Sharini, S. Jafari Khairabadi, and F. Naleini, "Radiomics-based machine learning for automated detection of Pneumothorax in CT scans." (in eng), Plos one, Vol. 19 (No. 12), p. e0314988, (2024).
26- Rakesh Katuwal and P. N. Suganthan, "An Ensemble of Kernel Ridge Regression for Multi-class Classification." Procedia Computer Science, Vol. 108pp. 375-83, (2017).
27- Xindong Wu et al., "Top 10 algorithms in data mining." Knowledge and Information Systems, Vol. 14 (No. 1), pp. 1-37, 2008/01/01 (2008).
28- Feng De-Cheng et al., "Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach." Construction and Building Materials, Vol. 230p. 117000, (2020).
29- Warut Pannakkong, Kwanluck Thiwa-Anont, Kasidit Singthong, Parthana Parthanadee, and Jirachai Buddhakulsomsiri, "Hyperparameter Tuning of Machine Learning Algorithms Using Response Surface Methodology: A Case Study of ANN, SVM, and DBN." Mathematical Problems in Engineering, Vol. 2022 (No. 1), p. 8513719, (2022).
30- Justus A Ilemobayo et al., "Hyperparameter Tuning in Machine Learning: A Comprehensive Review." Journal of Engineering Research and Reports, Vol. 26 (No. 6), pp. 388-95, 06/07 (2024).
31- Christian Arnold, Luka Biedebach, Andreas Küpfer, and Marcel Neunhoeffer, "The role of hyperparameters in machine learning models and how to tune them." Political Science Research and Methods, pp. 1-8, (2023).
32- Seongho Jo and Kipyo Kim, "CT-Based Radiomic Feature Analysis for Identifying Baseline Kidney Function in Patients With Underlying CKD: SA-PO915." Journal of the American Society of Nephrology, Vol. 33 (No. 11S), pp. 856-57, (2022).
33- Y. H. Choi et al., "Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy." (in eng), BMC Med Imaging, Vol. 24 (No. 1), p. 256, Sep 27 (2024).
34- Samira Jafari, Afshin Almasi, Hamid Sharini, Sajad Heydari, and Nader Salari, "Diagnosis of borderline personality disorder based on Cyberball social exclusion task and resting-state fMRI: using machine learning approach as an auxiliary tool." Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp. 1-11, (2022).
35- Y. Baştanlar and M. Ozuysal, "Introduction to machine learning." (in eng), Methods Mol Biol, Vol. 1107pp. 105-28, (2014).
36- Petrone Patrizio, Perez-Calvo Javier, E. M. Brathwaite Collin, Islam Shahidul, and K. Joseph D'Andrea, "Traumatic kidney injuries: A systematic review and meta-analysis." International Journal of Surgery, Vol. 74pp. 13-21, (2020).
37- Robert J McDonald et al., "The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload." Academic Radiology, Vol. 22 (No. 9), pp. 1191-98, (2015).
38- Giles WL Boland, Alexander S Guimaraes, and Peter R Mueller, "The radiologist’s conundrum: benefits and costs of increasing CT capacity and utilization." European radiology, Vol. 19pp. 9-11, (2009).
39- R. J. Kate, R. M. Perez, D. Mazumdar, K. S. Pasupathy, and V. Nilakantan, "Prediction and detection models for acute kidney injury in hospitalized older adults." (in eng), BMC Med Inform Decis Mak, Vol. 16p. 39, Mar 29 (2016).
40- Felix Y Yap et al., "Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses." European radiology, Vol. 31pp. 1011-21, (2021).
41- Johannes Uhlig et al., "Radiomic features and machine learning for the discrimination of renal tumor histological subtypes: a pragmatic study using clinical-routine computed tomography." Cancers, Vol. 12 (No. 10), p. 3010, (2020).
42- M. Cellina et al., "Artificial Intelligence in Emergency Radiology: Where Are We Going?" (in eng), Diagnostics (Basel), Vol. 12 (No. 12), Dec 19 (2022).
43- Firouz Amani, Masoud Amanzadeh, Mahnaz Hamedan, and Paniz Amani, "Diagnostic accuracy of deep learning in prediction of osteoporosis: a systematic review and meta-analysis." BMC Musculoskeletal Disorders, Vol. 25 (No. 1), p. 991, 2024/12/04 (2024).
44- Ali Tarighatnia, Masoud Amanzadeh, Mahnaz Hamedan, Alireza Mohammadnia, and Nader D. Nader, "Deep learning-based evaluation of panoramic radiographs for osteoporosis screening: a systematic review and meta-analysis." BMC Medical Imaging, Vol. 25 (No. 1), p. 86, 2025/03/12 (2025).
45- Quan-shi Zhang and Song-Chun Zhu, "Visual interpretability for deep learning: a survey." Frontiers of Information Technology & Electronic Engineering, Vol. 19 (No. 1), pp. 27-39, (2018).
46- Matthew D Zeiler and Rob Fergus, "Visualizing and understanding convolutional networks." in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13, (2014): Springer, pp. 818-33.
2- M. McPhee, N. Arumainayagam, M. Clark, N. Burfitt, and R. DasGupta, "Renal injury management in an urban trauma centre and implications for urological training." (in eng), Ann R Coll Surg Engl, Vol. 97 (No. 3), pp. 194-7, Apr (2015).
3- M. T. Heller and N. Schnor, "MDCT of renal trauma: correlation to AAST organ injury scale." (in eng), Clin Imaging, Vol. 38 (No. 4), pp. 410-17, Jul-Aug (2014).
4- T. Ząbkowski, R. Skiba, M. Saracyn, and H. Zieliński, "Analysis of Renal Trauma in Adult Patients: A 6-Year Own Experiences of Trauma Center." (in eng), Urol J, Vol. 12 (No. 4), pp. 2276-9, Sep 4 (2015).
5- T. Erlich and N. D. Kitrey, "Renal trauma: the current best practice." (in eng), Ther Adv Urol, Vol. 10 (No. 10), pp. 295-303, Oct (2018).
6- B. B. Voelzke and L. Leddy, "The epidemiology of renal trauma." (in eng), Transl Androl Urol, Vol. 3 (No. 2), pp. 143-9, Jun (2014).
7- W. Szmigielski, R. Kumar, S. Al Hilli, and M. Ismail, "Renal trauma imaging: Diagnosis and management. A pictorial review." (in eng), Pol J Radiol, Vol. 78 (No. 4), pp. 27-35, Oct (2013).
8- Xiang Li et al., "Deep learning-enabled system for rapid pneumothorax screening on chest CT." European journal of radiology, Vol. 120p. 108692, (2019).
9- H. P. Chan, R. K. Samala, and L. M. Hadjiiski, "CAD and AI for breast cancer-recent development and challenges." (in eng), Br J Radiol, Vol. 93 (No. 1108), p. 20190580, Apr (2020).
10- S. Wang and R. M. Summers, "Machine learning and radiology." Med Image Anal, Vol. 16 (No. 5), pp. 933-51, Jul (2012).
11- Sheetal Chaudhuri et al., "Artificial intelligence enabled applications in kidney disease." in Seminars in dialysis, (2021), Vol. 34 (No. 1): Wiley Online Library, pp. 5-16.
12- Nafiseh Ghaffar Nia, Erkan Kaplanoglu, and Ahad Nasab, "Evaluation of artificial intelligence techniques in disease diagnosis and prediction." Discover Artificial Intelligence, Vol. 3 (No. 1), p. 5, 2023/01/30 (2023).
13- H. Alimiri Dehbaghi, K. Khoshgard, H. Sharini, and S. J. Khairabadi, "Diagnosis of traumatic liver injury on computed tomography using machine learning algorithms and radiomics features: The role of artificial intelligence for rapid diagnosis in emergency rooms." (in eng), J Res Med Sci, Vol. 29p. 77, (2024).
14- Robert J Gillies, Paul E Kinahan, and Hedvig Hricak, "Radiomics: images are more than pictures, they are data." Radiology, Vol. 278 (No. 2), pp. 563-77, (2016).
15- Marius E Mayerhoefer et al., "Introduction to radiomics." Journal of Nuclear Medicine, Vol. 61 (No. 4), pp. 488-95, (2020).
16- Ruimeng Yang et al., "Radiomics of small renal masses on multiphasic CT: accuracy of machine learning–based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat." European radiology, Vol. 30pp. 1254-63, (2020).
17- Philippe Lambin et al., "Radiomics: extracting more information from medical images using advanced feature analysis." European journal of cancer, Vol. 48 (No. 4), pp. 441-46, (2012).
18- Hossein Naseri et al., "Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest." Scientific Reports, Vol. 12 (No. 1), p. 9866, 2022/06/14 (2022).
19- Andrew Laine and Jian Fan, "Texture classification by wavelet packet signatures." IEEE Transactions on pattern analysis and machine intelligence, Vol. 15 (No. 11), pp. 1186-91, (1993).
20- Alex Zwanenburg et al., "The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping." Radiology, Vol. 295 (No. 2), pp. 328-38, (2020).
21- Guillaume Thibault, Jesus Angulo, and Fernand Meyer, "Advanced statistical matrices for texture characterization: application to cell classification." IEEE Transactions on Biomedical Engineering, Vol. 61 (No. 3), pp. 630-37, (2013).
22- Ethem Alpaydin, Introduction to machine learning. MIT press, (2020).
23- Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). (2009).
24- Baoyang Cui, Zhonglin Ye, Haixing Zhao, Zhuome Renqing, Lei Meng, and Yanlin Yang, "Used Car Price Prediction Based on the Iterative Framework of XGBoost+LightGBM." Electronics, Vol. 11 (No. 18), p. 2932, (2022).
25- H. Alimiri Dehbaghi, K. Khoshgard, H. Sharini, S. Jafari Khairabadi, and F. Naleini, "Radiomics-based machine learning for automated detection of Pneumothorax in CT scans." (in eng), Plos one, Vol. 19 (No. 12), p. e0314988, (2024).
26- Rakesh Katuwal and P. N. Suganthan, "An Ensemble of Kernel Ridge Regression for Multi-class Classification." Procedia Computer Science, Vol. 108pp. 375-83, (2017).
27- Xindong Wu et al., "Top 10 algorithms in data mining." Knowledge and Information Systems, Vol. 14 (No. 1), pp. 1-37, 2008/01/01 (2008).
28- Feng De-Cheng et al., "Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach." Construction and Building Materials, Vol. 230p. 117000, (2020).
29- Warut Pannakkong, Kwanluck Thiwa-Anont, Kasidit Singthong, Parthana Parthanadee, and Jirachai Buddhakulsomsiri, "Hyperparameter Tuning of Machine Learning Algorithms Using Response Surface Methodology: A Case Study of ANN, SVM, and DBN." Mathematical Problems in Engineering, Vol. 2022 (No. 1), p. 8513719, (2022).
30- Justus A Ilemobayo et al., "Hyperparameter Tuning in Machine Learning: A Comprehensive Review." Journal of Engineering Research and Reports, Vol. 26 (No. 6), pp. 388-95, 06/07 (2024).
31- Christian Arnold, Luka Biedebach, Andreas Küpfer, and Marcel Neunhoeffer, "The role of hyperparameters in machine learning models and how to tune them." Political Science Research and Methods, pp. 1-8, (2023).
32- Seongho Jo and Kipyo Kim, "CT-Based Radiomic Feature Analysis for Identifying Baseline Kidney Function in Patients With Underlying CKD: SA-PO915." Journal of the American Society of Nephrology, Vol. 33 (No. 11S), pp. 856-57, (2022).
33- Y. H. Choi et al., "Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy." (in eng), BMC Med Imaging, Vol. 24 (No. 1), p. 256, Sep 27 (2024).
34- Samira Jafari, Afshin Almasi, Hamid Sharini, Sajad Heydari, and Nader Salari, "Diagnosis of borderline personality disorder based on Cyberball social exclusion task and resting-state fMRI: using machine learning approach as an auxiliary tool." Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp. 1-11, (2022).
35- Y. Baştanlar and M. Ozuysal, "Introduction to machine learning." (in eng), Methods Mol Biol, Vol. 1107pp. 105-28, (2014).
36- Petrone Patrizio, Perez-Calvo Javier, E. M. Brathwaite Collin, Islam Shahidul, and K. Joseph D'Andrea, "Traumatic kidney injuries: A systematic review and meta-analysis." International Journal of Surgery, Vol. 74pp. 13-21, (2020).
37- Robert J McDonald et al., "The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload." Academic Radiology, Vol. 22 (No. 9), pp. 1191-98, (2015).
38- Giles WL Boland, Alexander S Guimaraes, and Peter R Mueller, "The radiologist’s conundrum: benefits and costs of increasing CT capacity and utilization." European radiology, Vol. 19pp. 9-11, (2009).
39- R. J. Kate, R. M. Perez, D. Mazumdar, K. S. Pasupathy, and V. Nilakantan, "Prediction and detection models for acute kidney injury in hospitalized older adults." (in eng), BMC Med Inform Decis Mak, Vol. 16p. 39, Mar 29 (2016).
40- Felix Y Yap et al., "Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses." European radiology, Vol. 31pp. 1011-21, (2021).
41- Johannes Uhlig et al., "Radiomic features and machine learning for the discrimination of renal tumor histological subtypes: a pragmatic study using clinical-routine computed tomography." Cancers, Vol. 12 (No. 10), p. 3010, (2020).
42- M. Cellina et al., "Artificial Intelligence in Emergency Radiology: Where Are We Going?" (in eng), Diagnostics (Basel), Vol. 12 (No. 12), Dec 19 (2022).
43- Firouz Amani, Masoud Amanzadeh, Mahnaz Hamedan, and Paniz Amani, "Diagnostic accuracy of deep learning in prediction of osteoporosis: a systematic review and meta-analysis." BMC Musculoskeletal Disorders, Vol. 25 (No. 1), p. 991, 2024/12/04 (2024).
44- Ali Tarighatnia, Masoud Amanzadeh, Mahnaz Hamedan, Alireza Mohammadnia, and Nader D. Nader, "Deep learning-based evaluation of panoramic radiographs for osteoporosis screening: a systematic review and meta-analysis." BMC Medical Imaging, Vol. 25 (No. 1), p. 86, 2025/03/12 (2025).
45- Quan-shi Zhang and Song-Chun Zhu, "Visual interpretability for deep learning: a survey." Frontiers of Information Technology & Electronic Engineering, Vol. 19 (No. 1), pp. 27-39, (2018).
46- Matthew D Zeiler and Rob Fergus, "Visualizing and understanding convolutional networks." in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13, (2014): Springer, pp. 818-33.
| Files | ||
| Issue | Vol 13 No 3 (2026) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v13i3.22735 | |
| Keywords | ||
| Kidney, Blunt Trauma, Radiomics, CT scan, Machine Learning, Artificial Intelligence | ||
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How to Cite
1.
Alimiri Dehbaghi H, Khoshgard K, Jafari Khairabadi S. Intelligent Diagnosis in Trauma: Exploring Machine Learning and Radiomics for Kidney Injury Assessment. Frontiers Biomed Technol. 2026;13(3):628-640.
