Journal of Surgery Concepts & Practice >
Innovation in the era of big data: advancing abdominal wall mechanics research through machine learning and artificial intelligence
Received date: 2024-06-26
Online published: 2024-11-15
Research in abdominal wall mechanics is progressively overcoming the limitations of traditional assessment methods with the application of machine learning and artificial intelligence technologies. By leveraging deep learning algorithms and big data analytics, precise mechanical and predictive models are being established to analyze the stress distribution in abdominal wall muscles under various conditions, facilitating the development of personalized treatment strategies. This approach not only aids in optimizing hernia repair strategies and reducing recurrence risks, but also has the potential to improve patient outcomes. Looking ahead, the continued integration of multidimensional data will further drive systematic research and clinical application in the field of abdominal wall mechanics.
MAO Minghuan , YANG Binze , PENG Xueqiang , LI Hangyu . Innovation in the era of big data: advancing abdominal wall mechanics research through machine learning and artificial intelligence[J]. Journal of Surgery Concepts & Practice, 2024 , 29(04) : 300 -303 . DOI: 10.16139/j.1007-9610.2024.04.05
| [1] | ALIOTTA R E, GATHERWRIGHT J, KRPATA D, et al. Complex abdominal wall reconstruction, harnessing the power of a specialized multidisciplinary team to improve pain and quality of life[J]. Hernia, 2019, 23(2):205-215. |
| [2] | BATISTA G A, BELTRáN S P, PASSOS M H P D, et al. Comparison of the electromyography activity during exercises with stable and unstable surfaces: a systematic review and meta-analysis[J]. Sports (Basel), 2024, 12(4):111. |
| [3] | DEERENBERG E B, HENRIKSEN N A, ANTONIOU G A, et al. Updated guideline for closure of abdominal wall incisions from the European and American hernia socie-ties[J]. Br J Surg, 2022, 109(12):1239-1250. |
| [4] | GUNNARSSON U, JOHANSSON M, STRIG?RD K. Assessment of abdominal muscle function using the Biodex System-4. validity and reliability in healthy volunteers and patients with giant ventral hernia[J]. Hernia, 2011, 15(4):417-421. |
| [5] | STARK B, EMANUELSSON P, GUNNARSSON U, et al. Validation of Biodex system 4 for measuring the strength of muscles in patients with rectus diastasis[J]. J Plast Surg Hand Surg, 2012, 46(2):102-105. |
| [6] | GUEROULT P, JOPPIN V, CHAUMOITRE K, et al. Linea alba 3D morphometric variability by CT scan exploration[J]. Hernia, 2024, 28(2):485-494. |
| [7] | CUI P, ZHAO S, CHEN W. Identification of the vas de-ferens in laparoscopic inguinal hernia repair surgery using the convolutional neural network[J]. J Healthc Eng, 2021,2021:5578089. |
| [8] | JOURDAN A, DHUME R, GUéRIN E, et al. Numerical investigation of a finite element abdominal wall model during breathing and muscular contraction[J]. Comput Methods Programs Biomed, 2024,244:107985. |
| [9] | ZHANG Q, CHEN Z, PENG Y, et al. The novel magnesium-titanium hybrid cannulated screws for the treatment of vertical femoral neck fractures: biomechanical evaluation[J]. J Orthop Translat, 2023,42:127-136. |
| [10] | CHARVáTOVá H, EAST B, PROCHáZKA A, et al. Computational analysis and classification of hernia repairs[J]. Appl Sci, 2024, 14(8):3236. |
| [11] | RELLE J J, VO? S, RASCHIDI R, et al. HEDI: first-time clinical application and results of a biomechanical evaluation and visualisation tool for incisional hernia repair[J]. ArXiv.org,2023:2307.01502. |
| [12] | OKORJI L M, GIRI O, LUQUE-SANCHEZ K, et al. Computed tomography measurements to predict need for robotic transversus abdominis release: a single institution analysis[J]. Hernia, 2024, 28(5):1649-1655. |
| [13] | KARAMI M, ZOHOOR H, CALVO B, et al. A 3D multi-scale skeletal muscle model to predict active and passive responses. Application to intra-abdominal pressure prediction[J]. Comput Methods Appl Mech Eng, 2023,415:116222. |
| [14] | LESCH C, NESSEL R, ADOLF D, et al. STRONGHOLD first-year results of biomechanically calculated abdominal wall repair: a propensity score matching[J]. Hernia, 2024, 28(1):63-73. |
| [15] | ELHAGE S A, DEERENBERG E B, AYUSO S A, et al. Development and validation of image-based deep learning models to predict surgical complexity and complications in abdominal wall reconstruction[J]. JAMA Surg, 2021, 156(10):933-940. |
| [16] | TUSET L, LóPEZ-CANO M, FORTUNY G, et al. A virtual simulation approach to assess the effect of trocar-site placement and scar characteristics on the abdominal wall biomechanics[J]. Sci Rep, 2024, 14(1):3583. |
| [17] | TUSET L, LóPEZ-CANO M, FORTUNY G, et al. Virtual simulation of the biomechanics of the abdominal wall with different stoma locations[J]. Sci Rep, 2022, 12(1):3545. |
| [18] | EMMERZAAL J, DE BRABANDERE A, VAN DER STRAATEN R, et al. Can the output of a learned classification model monitor a person’s functional recovery status post-total knee arthroplasty?[J]. Sensors (Basel), 2022, 22(10):3698. |
/
| 〈 |
|
〉 |