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 t...
| Publicado en: | Archives of Academic Emergency Medicine Vol. 13; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Shahid Beheshti University of Medical Sciences
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190500670&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190500670 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26454904 MFMK jtl: Archives of Academic Emergency Medicine issn: 26454904 maglogo: N pubinfo: dt: 2025 vid: 13 iid: 1 pid: 87963 pub: Shahid Beheshti University of Medical Sciences artinfo: ui: 190500670 190500670 190500670 10.22037/aaemj.v13i1.2479 190500670 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Detection of Body Packs in Abdominal CT scans Through Artificial Intelligence; Developing a Machine Learning-based Model. aug: au: Hosseini, Sayed Masoud Mohtarami, Seyed Ali Shadnia, Shahin Rahimi, Mitra Erfan Talab Evini, Peyman Mostafazadeh, Babak Memarian, Azadeh Heidarli, Elmira affil: Toxicological Research Center, Excellence Center of Clinical Toxicology, Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. sug: subj: Street Drugs Foreign Bodies Radiography Radiography, Abdominal Tomography, X-Ray Computed Artificial Intelligence Image Processing, Computer Assisted Machine Learning Algorithms Detection Algorithms Human Cross Sectional Studies Precision Sensitivity and Specificity Descriptive Statistics Poisoning Funding Source Convolutional Neural Networks Software Iran Academic Medical Centers Adolescence Adult Middle Age Aged Aged, 80 and Over Videorecording Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over ab: 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 crosssectional 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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