Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, Kim N (2017) Deep learning in medical imaging: general overview. Korean J Radiol. 18(4):570–584. https://doi.org/10.3348/kjr.2017.18.4.570

Article  PubMed  PubMed Central  Google Scholar 

Liu J, Varghese B, Taravat F, Eibschutz LS, Gholamrezanezhad A (2022) An extra set of intelligent eyes: application of artificial intelligence in imaging of abdominopelvic pathologies in emergency radiology. Diagnostics. 12(6):1351. https://doi.org/10.3390/diagnostics12061351

Article  PubMed  PubMed Central  Google Scholar 

Behzadi-Khormouji H, Rostami H, Salehi S, Derakhshande-Rishehri T, Masoumi M, Salemi S, Keshavarz A, Gholamrezanezhad A, Assadi M, Batouli A (2020) Deep learning, reusable and problem-based architectures for detection of consolida-tion on chest X-ray images. Comput Methods Programs Biomed 185:105162. https://doi.org/10.1016/j.cmpb.2019.105162. (Epub 2019 Oct 31 PMID: 31715332)

Article  PubMed  Google Scholar 

Hazarika I (2020Jul 1) Artificial intelligence: opportunities and implications for the health workforce. Int Health 12(4):241–245. https://doi.org/10.1093/inthealth/ihaa007.PMID:32300794;PMCID:PMC7322190

Article  PubMed  PubMed Central  Google Scholar 

Zejnullahu VA, Bicaj BX, Zejnullahu VA, Hamza AR (2017) Retained surgical foreign bodies after surgery. Open Access Maced J Med Sci 5(1):97–100. https://doi.org/10.3889/oamjms.2017.005

Article  PubMed  PubMed Central  Google Scholar 

Williams TL, Tung DK, Steelman VM, Chang PK, Szekendi MK (2014) Retained surgical sponges: findings from incident reports and a cost-benefit analysis of radiofrequency technology. J Am Coll Surg 219(3):354–364

Article  PubMed  Google Scholar 

Goldberg JL, Feldman DL (2012) Implementing AORN recommended practices for prevention of retained Surgical Items. AORN J 95:205–219

Article  PubMed  Google Scholar 

Gibbs VC, Coakley FD, Reines HD (2007) Preventable errors in the operating room: retained foreign bodies after surgery–Part I. Curr Probl Surg 44(5):281–337. https://doi.org/10.1067/j.cpsurg.2007.03.002.PMid:17512832

Article  PubMed  Google Scholar 

Kumar GVS, Ramani S, Mahajan A, Jain N, Sequeira R, Thakur M (2017) Imaging of retained surgical items: A pictorial review including new innovations. Indian J Radiol Imaging 27(3):354–361. https://doi.org/10.4103/ijri.IJRI_31_17

Article  PubMed  PubMed Central  Google Scholar 

O’Connor AR, Coakley FV, Meng MV, Eberhardt SC (2003) Imaging of retained surgical sponges in the abdomen and pelvis. AJR Am J Roentgenol 180(2):481–489. https://doi.org/10.2214/ajr.180.2.1800481

Article  PubMed  Google Scholar 

Yun G, Kazerooni EA, Lee EM, Shah PN, Deeb M, Agarwal PP (2021) Retained surgical items at chest imaging. Radiographics. 41(2):E10–E11. https://doi.org/10.1148/rg.2021200128

Article  PubMed  Google Scholar 

Cima RR, Kollengode A, Garnatz J, Storsveen A, Weisbrod C, Deschamps C (2008) Incidence and characteristics of potential and actual retained foreign object events in surgical patients. J Am Coll Surg 207(1):80–87. https://doi.org/10.1016/j.jamcollsurg.2007.12.047

Article  PubMed  Google Scholar 

Pole G, Thomas B (2017) A Pictorial Review of the Many Faces of Gossypiboma - Observations in 6 Cases. Pol J Radiol 82:418–421. https://doi.org/10.12659/PJR.900745

Article  PubMed  PubMed Central  Google Scholar 

Modrzejewski A, Kaźmierczak KM, Kowalik K, Grochal I (2023) Surgical items retained in the abdominal cavity in diagnostic imaging tests: a series of 10 cases and literature review. Pol J Radiol 88:264–269. https://doi.org/10.5114/pjr.2023.127668

Article  Google Scholar 

Wan W, Le T, Riskin L, Macario A (2009) Improving safety in the operating room: a systematic literature review of retained surgical sponges. Curr Opin Anaesthesiol 22(2):207–214. https://doi.org/10.1097/ACO.0b013e328324f82d

Article  PubMed  Google Scholar 

Lu YY, Cheung YC, Ko SF, Ng SH (2005) Calcified reticulate rind sign: A characteristic feature of gossypiboma on computed tomography. World J Gastroenterol 11:4927–4929

Article  PubMed  PubMed Central  Google Scholar 

Pennsylvania Patient Safety Authority (no date) Retained surgical items: Events and guidelines revisited: Advisory, Pennsylvania Patient Safety Authority. Available at: https://patientsafety.pa.gov/ADVISORIES/Pages/201703_RSI.aspx. Accessed 19 Mar 2024

Yamaguchi S, Soyama A, Ono S, Hamauzu S, Yamada M, Fukuda T, Hidaka M, Tsurumoto T, Uetani M, Eguchi S (2021) Novel computer-aided diagnosis software for the prevention of retained surgical items. J Am Coll Surg 233(6):686–696

Article  PubMed  Google Scholar 

Kawakubo M, Waki H, Shirasaka T, Kojima T, Mikayama R, Hamasaki H, Akamine H, Kato T, Baba S, Ushiro S, Ishigami K (2023) A deep learning model based on fusion images of chest radiography and X-ray sponge images supports human visual characteristics of retained surgical items detection. Int J Comput Assist Radiol Surg 18(8):1459–1467

Article  PubMed  Google Scholar 

Ponrartana S, Coakley FV, Yeh BM et al (2008) Accuracy of plain abdominal radiographs in the detection of retained surgical needles in the peritoneal cavity. Ann Surg 247(1):8–12. https://doi.org/10.1097/SLA.0b013e31812eeca5

Article  PubMed  Google Scholar 

Egorova NN, Moskowitz A, Gelijns A et al (2008) Managing the prevention of retained surgical instruments: what is the value of counting? Ann Surg 247:13–18

Article  PubMed  Google Scholar 

Asiyanbola B, Cheng-Wu C, Lewin JS, Etienne-Cummings R (2012) Modified Map-Seeking Circuit: Use of Computer-Aided Detection in Locating Postoperative Retained Foreign Bodies1. J Surg Res 175(2):47–52. https://doi.org/10.1016/j.jss.2011.11.1018

Article  Google Scholar 

Sengupta A, Hadjiiski L, Chan HP, Cha K, Chronis N, Marentis TC (2017) Computer-aided detection of retained surgical needles from postoperative radiographs. Med Phys 44(1):180–191. https://doi.org/10.1002/mp.12011

Article  PubMed  Google Scholar 

Tripathi A, Marentis T, Chronis N (2012) Microfrabricated instrument tag for the radiographic detection of retained foreign bodies during surgery, SPIE

Marentis TC, Davenport MS, Dillman JR, Sanchez R, Kelly AM, Cronin P, DeFreitas MR, Hadjiiski L, Chan HP (2018) Interrater Agreement and Diagnostic Accuracy of a Novel Computer-Aided Detection Process for the Detection and Prevention of Retained Surgical Instruments. AJR Am J Roentgenol 210(4):709–714. https://doi.org/10.2214/AJR.17.18576

Asensio JA, Verde JM (2012) Penetrating Wounds. In: Vincent JL, Hal JB (eds) l Encyclopedia of Intensive Care Medicine. Springer, Berlin, Heidelberg, pp 1699–1703

Google Scholar 

Kaufman EJ, Wiebe DJ, Xiong RA, Morrison CN, Seamon MJ, Delgado MK (2021) Epidemiologic Trends in Fatal and Nonfatal Firearm Injuries in the US, 2009–2017. JAMA Intern Med 181(2):237–244

Article  PubMed  Google Scholar 

(2023) Web-based injury statistics query and reporting system (WISQARS), Centers for Disease Control and Prevention.

Bryczkowski C (2020) Foreign body localization, Sonoguide. Available at: https://www.acep.org/sonoguide/procedures/foreign-bodies. Accessed 18 Dec 2023

Lewis D, Jivraj A, Atkinson P, Jarman R (2015) My patient is injured: identifying foreign bodies with ultrasound. Ultrasound 23(3):174–180. https://doi.org/10.1177/1742271X15579950. Accessed 19 Dec 2023

Shelhoss SC, Burgin CM (2022) Maximizing Foreign Body Detection by Ultrasound With the Water Bath Technique Coupled With the Focal Zone Advantage: A Technical Report. Cureus 14(11):e31577. https://doi.org/10.7759/cureus.31577

Article  PubMed  PubMed Central  Google Scholar 

Voss JO, Maier C, Wüster J et al (2021) Imaging foreign bodies in head and neck trauma: a pictorial review. Insights Imaging 12:20. https://doi.org/10.1186/s13244-021-00969-9

Article  PubMed  PubMed Central  Google Scholar 

Rupert J, Honeycutt JD, Odom MR (2020) Foreign bodies in the skin: evaluation and management. Am Fam Physician 101(12):740–747

PubMed  Google Scholar 

Bukur M, Green DJ (2012) Plain x-rays for penetrating trauma. In: Velmahos G, Degiannis E, Doll D (eds) Penetrating trauma. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20453-1_13

Campbell EA, Wilbert CD (2023) Foreign Body Imaging. [Updated 2023 Jul 30]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing Available from: https://www.ncbi.nlm.nih.gov/books/NBK470294/

Gopireddy DR, Kee-Sampson JW, Vulasala SSR, Stein R, Kumar S, Virarkar M (2023) Imaging of penetrating vascular trauma of the body and extremities secondary to ballistic and stab wounds. J Clin Imaging Sci 13:1

Article  PubMed  PubMed Central  Google Scholar 

Ballard DH, Naeem M, Hoegger MJ, Rajput MZ, Mellnick VM (2020) Imaging of Penetrating Abdominal and Pelvic Trauma. In: Patlas MN, Katz DS, Scaglione M (eds) Atlas of Emergency Imaging from Head-to-Toe. Springer International Publishing, Cham, pp 1–17

Google Scholar 

Gunn ML, Clark RT, Sadro CT, Linnau KF, Sandstrom CK (2014) Current concepts in imaging evaluation of penetrating transmediastinal injury. Radiographics 34(7):1824–1841

Article  PubMed  Google Scholar 

Ogunyemi O, Clarke JR, Webber B, Badler N (2000) "TraumaSCAN: assessing penetrating trauma with geometric and probabilistic reasoning." Proc AMIA Symp: 620–624

Ogunyemi O (2006) Methods for reasoning from geometry about anatomic structures injured by penetrating trauma. J Biomed Inform 39(4):389–400

Article  PubMed  Google Scholar 

Matheny ME, Ogunyemi OI, Rice PL, Clarke JR (2005) Evaluating the discriminatory power of a computer-based system for assessing penetrating trauma on retrospective multi-center data. AMIA Annu Symp Proc 2005:500–504

PubMed  PubMed Central  Google Scholar 

Ahmed BA, Matheny ME, Rice PL, Clarke JR, Ogunyemi OI (2009) A comparison of methods for assessing penetrating trauma on retrospective multi-center data. J Biomed Inform 42(2):308–316

Article  PubMed  Google Scholar 

Steenburg SD, Sliker CW, Shanmuganathan K, Siegel EL (2010) Imaging evaluation of penetrating neck injuries. Radiographics 30(4):869–886

Article  PubMed  Google Scholar 

Yi X, Adams SJ, Henderson RDE, Babyn P (2020) Computer-aided assessment of catheters and tubes on radiographs: how good is artificial intelligence for assessment? Radiol Artif Intell 2(1):e190082

Article  PubMed  PubMed Central  Google Scholar 

Wang CH, Hwang T, Huang YS, Tay J, Wu CY, Wu MC et al (2024) Deep learning-based localization and detection of malpositioned endotracheal tube on portable supine chest radiographs in intensive and emergency medicine: a multicenter retrospective study. Crit Care Med 52(2):237–247