Detection of Body Packs in Abdominal CT scans Through Artificial Intelligence; Developing a Machine Learning-based Model
Introduction: Identifying the people who try to hide illegal substances in the body for smuggling is of considerable importance in forensic medicine and poisoning. This study aimed to develop a new diagnostic method using artificial intelligence to detect body packs in real-time Abdominal computed...
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| Format: | Article |
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Shahid Beheshti University of Medical Sciences
2024-12-01
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| Series: | Archives of Academic Emergency Medicine |
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| Online Access: | https://journals.sbmu.ac.ir/aaem/index.php/AAEM/article/view/2479 |
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| author | Sayed Masoud Hosseini Seyed Ali Mohtarami Shahin Shadnia Mitra Rahimi Peyman Erfan Talab Evini Babak Mostafazadeh Azadeh Memarian Elmira Heidarli |
| author_facet | Sayed Masoud Hosseini Seyed Ali Mohtarami Shahin Shadnia Mitra Rahimi Peyman Erfan Talab Evini Babak Mostafazadeh Azadeh Memarian Elmira Heidarli |
| author_sort | Sayed Masoud Hosseini |
| collection | DOAJ |
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Introduction: Identifying the people who try to hide illegal substances in the body for smuggling is of considerable importance in forensic medicine and poisoning. This study aimed to develop a new diagnostic method using artificial intelligence to detect body packs in real-time Abdominal computed tomography (CT) scans.
Methods: In this cross-sectional study, abdominal CT scan images were employed to create a machine learning-based model for detecting body packs. A single-step object detection called RetinaNet using a modified neck (Proposed Model) was performed to achieve the best results. Also, an angled Bbox (oriented bounding box) in the training dataset played an important role in improving the results.
Results: A total of 888 abdominal CT scan images were studied. Our proposed Body Packs Detection (BPD) model achieved a mean average precision (mAP) value of 86.6% when the intersection over union (IoU) was 0.5, and a mAP value of 45.6% at different IoU thresholds (from 0.5 to 0.95 in steps of 0.05). It also obtained a Recall value of 58.5%, which was the best result among the standard object detection methods such as the standard RetinaNet.
Conclusion: This study employed a deep learning network to identify body packs in abdominal CT scans, highlighting the importance of incorporating object shape and variability when leveraging artificial intelligence in healthcare to aid medical practitioners. Nonetheless, the development of a tailored dataset for object detection, like body packs, requires careful curation by subject matter specialists to ensure successful training.
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| format | Article |
| id | doaj-art-e9c9b358bb0b420197fe7f7bbb959990 |
| institution | OA Journals |
| issn | 2645-4904 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Shahid Beheshti University of Medical Sciences |
| record_format | Article |
| series | Archives of Academic Emergency Medicine |
| spelling | doaj-art-e9c9b358bb0b420197fe7f7bbb9599902025-08-20T02:26:14ZengShahid Beheshti University of Medical SciencesArchives of Academic Emergency Medicine2645-49042024-12-0113110.22037/aaemj.v13i1.2479Detection of Body Packs in Abdominal CT scans Through Artificial Intelligence; Developing a Machine Learning-based ModelSayed Masoud Hosseini0Seyed Ali Mohtarami1Shahin Shadnia2Mitra Rahimi3Peyman Erfan Talab EviniBabak MostafazadehAzadeh Memarian4Elmira Heidarli5 Toxicological Research Center, Excellence Center of Clinical Toxicology, Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, IranDepartment of Computer Engineering and Information Technology, (PNU), Tehran, IranToxicological Research Center, Excellence Center of Clinical Toxicology, Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, IranToxicological Research Center, Excellence Center of Clinical Toxicology, Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, IranEmergency Medicine, School of Medicine, Mazandaran University of Medical Sciences, Sari, IranSchool of Pharmacy, Shahaid Beheshti University of Medical Sciences Introduction: Identifying the people who try to hide illegal substances in the body for smuggling is of considerable importance in forensic medicine and poisoning. This study aimed to develop a new diagnostic method using artificial intelligence to detect body packs in real-time Abdominal computed tomography (CT) scans. Methods: In this cross-sectional study, abdominal CT scan images were employed to create a machine learning-based model for detecting body packs. A single-step object detection called RetinaNet using a modified neck (Proposed Model) was performed to achieve the best results. Also, an angled Bbox (oriented bounding box) in the training dataset played an important role in improving the results. Results: A total of 888 abdominal CT scan images were studied. Our proposed Body Packs Detection (BPD) model achieved a mean average precision (mAP) value of 86.6% when the intersection over union (IoU) was 0.5, and a mAP value of 45.6% at different IoU thresholds (from 0.5 to 0.95 in steps of 0.05). It also obtained a Recall value of 58.5%, which was the best result among the standard object detection methods such as the standard RetinaNet. Conclusion: This study employed a deep learning network to identify body packs in abdominal CT scans, highlighting the importance of incorporating object shape and variability when leveraging artificial intelligence in healthcare to aid medical practitioners. Nonetheless, the development of a tailored dataset for object detection, like body packs, requires careful curation by subject matter specialists to ensure successful training. https://journals.sbmu.ac.ir/aaem/index.php/AAEM/article/view/2479Artificial intelligenceCT-scanBody packerObject detectionOriented Bounding Box |
| spellingShingle | Sayed Masoud Hosseini Seyed Ali Mohtarami Shahin Shadnia Mitra Rahimi Peyman Erfan Talab Evini Babak Mostafazadeh Azadeh Memarian Elmira Heidarli Detection of Body Packs in Abdominal CT scans Through Artificial Intelligence; Developing a Machine Learning-based Model Archives of Academic Emergency Medicine Artificial intelligence CT-scan Body packer Object detection Oriented Bounding Box |
| title | Detection of Body Packs in Abdominal CT scans Through Artificial Intelligence; Developing a Machine Learning-based Model |
| title_full | Detection of Body Packs in Abdominal CT scans Through Artificial Intelligence; Developing a Machine Learning-based Model |
| title_fullStr | Detection of Body Packs in Abdominal CT scans Through Artificial Intelligence; Developing a Machine Learning-based Model |
| title_full_unstemmed | Detection of Body Packs in Abdominal CT scans Through Artificial Intelligence; Developing a Machine Learning-based Model |
| title_short | Detection of Body Packs in Abdominal CT scans Through Artificial Intelligence; Developing a Machine Learning-based Model |
| title_sort | detection of body packs in abdominal ct scans through artificial intelligence developing a machine learning based model |
| topic | Artificial intelligence CT-scan Body packer Object detection Oriented Bounding Box |
| url | https://journals.sbmu.ac.ir/aaem/index.php/AAEM/article/view/2479 |
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