Artificial intelligence in breast reconstruction

Abstract

Breast reconstruction is a critical component of breast cancer treatment. With the rapid integration of Artificial Intelligence (AI) into healthcare, its potential to revolutionize breast reconstruction has become increasingly evident. This narrative review examines the latest AI developments across the preoperative, intraoperative, and postoperative phases of breast reconstruction. In preoperative consultations, AI and augmented reality (AR)-driven simulations help both the surgeons and the patients visualize reconstruction outcomes. Imaging analysis and predictive modeling enhance the precision and efficiency of autologous procedures such as deep inferior epigastric artery perforator flap-based reconstruction. Within the operating room, AI applications such as real-time perforator mapping and AR modeling offer plastic surgeons improved control and visualization, which helps to reduce postoperative complications. Furthermore, AI models help surgeons design and deliver more personalized and value-based postoperative care, thereby improving patient satisfaction and overall cost-effectiveness. While AI applications demonstrate promising utility, challenges such as high costs, reliability, and the need for extensive clinical validation remain. Ongoing research and large-scale clinical trials are crucial to fully harness AI’s potential in improving breast reconstruction outcomes.

Keywords

Breast reconstruction / plastic surgery / artificial intelligence / augmented reality

References

[1] Roy N,Ibelli T.The psychological impacts of post-mastectomy breast reconstruction: a systematic review.Ann Breast Surg2024;8:19-19 PMCID:PMC11296521

[2] Mavioso C,Oliveira HP.Automatic detection of perforators for microsurgical reconstruction.Breast2020;50:19-24 PMCID:PMC7375543

[3] Lim B,Seth I.Evaluating artificial intelligence's role in teaching the reporting and interpretation of computed tomographic angiography for preoperative planning of the deep inferior epigastric artery perforator flap.JPRAS Open2024;40:273-85 PMCID:PMC11067004

[4] O'Neill AC,Roy M,Hofer SOP.Development and evaluation of a machine learning prediction model for flap failure in microvascular breast reconstruction.Ann Surg Oncol2020;27:3466-75

[5] Chen W,You L,Xu J.Artificial intelligence-based multimodal risk assessment model for surgical site infection (AMRAMS): development and validation study.JMIR Med Inform2020;8:e18186

[6] Chartier C,Lin O,Lee J.BreastGAN: artificial intelligence-enabled breast augmentation simulation.Aesthet Surg J Open Forum2022;4:ojab052 PMCID:PMC8781773

[7] La Padula S,D'Andrea F.Assessment of patient satisfaction using a new augmented reality simulation software for breast augmentation: a prospective study.J Clin Med2022;11:3464 PMCID:PMC9225128

[8] Hammond DC,Bageris MH.Use of three-dimensional imaging to assess the effectiveness of volume as a critical variable in breast implant selection.Plast Reconstr Surg2022;149:70-9

[9] Kwong JW,Choi S.Assessing the accuracy of a 3-dimensional surface imaging system in breast volume estimation.Ann Plast Surg2020;84:S311-7

[10] Amini S.Augmented reality mastectomy surgical planning prototype using the HoloLens template for healthcare technology letters.Healthc Technol Lett2019;6:261-5 PMCID:PMC6952247

[11] Stern CS,Rubenstein R.Three-dimensional surface analysis for preoperative prediction of breast volume: a validation study.Plast Reconstr Surg2023;152:1153-62 PMCID:PMC11404560

[12] Vles MD,Zijlstra K,Corten EML.Virtual and augmented reality for preoperative planning in plastic surgical procedures: a systematic review.J Plast Reconstr Aesthet Surg2020;73:1951-9

[13] Shafarenko MS,Hofer SOP.The role of augmented reality in the next phase of surgical education.Plast Reconstr Surg Glob Open2022;10:e4656 PMCID:PMC9633082

[14] Sacks JM,Broyles JM,Valerio IL.Near-infrared laser-assisted indocyanine green imaging for optimizing the design of the anterolateral thigh flap.Eplasty2012;12:e30 PMCID:PMC3392148

[15] Singaravelu A,Potter S.Artificial intelligence for optimum tissue excision with indocyanine green fluorescence angiography for flap reconstructions: proof of concept.JPRAS Open2024;41:389-93 PMCID:PMC11381603

[16] DeFazio MV,Ahmad FI.Application of three-dimensional printed vascular modeling as a perioperative guide to perforator mapping and pedicle dissection during abdominal flap harvest for breast reconstruction.J Reconstr Microsurg2020;36:325-38

[17] Meier EL,Hummelink S.Projected augmented reality in DIEP flap breast reconstruction: projecting perforators on the skin using dynamic infrared thermography.J Plast Reconstr Aesthet Surg2024;94:83-90

[18] Pinto-Coelho L.How artificial intelligence is shaping medical imaging technology: a survey of innovations and applications.Bioengineering2023;10:1435 PMCID:PMC10740686

[19] De La Hoz EC,Verspeek S.Automated thermographic detection of blood vessels for DIEP flap reconstructive surgery.Int J Comput Assist Radiol Surg2024;19:1733-41

[20] Saxena E.Deep learning for personalized preoperative planning of microsurgical free tissue transfers. In: Sycara K, Honavar V, Spaan M, editors. Proceedings of the 36th AAAI Conference on Artificial Intelligence; 2022 Feb 22-Mar 1; Vancouver, Canada. AAAI Press; 2022. p. 13140-1.

[21] AI-enabled segmentation | materialise mimics. Available from: https://www.materialise.com/en/healthcare/mimics-innovation-suite/ai-enabled-segmentation. [Last accessed on 30 Dec 2024]

[22] Seth I,Jakobsen A.Improving visualization of intramuscular perforator course: augmented reality headsets for DIEP flap breast reconstruction.Plast Reconstr Surg Glob Open2023;11:e5282 PMCID:PMC10513288

[23] Necker FN,Fischer MJ.HoloDIEP-faster and more accurate intraoperative DIEA perforator mapping using a novel mixed reality tool.J Reconstr Microsurg2024;

[24] Ghasroddashti A,Martou G.Utility of 3D-printed vascular modeling in microsurgical breast reconstruction: a systematic review.J Plast Reconstr Aesthet Surg2024;96:95-104

[25] Ballard DH,Duszak R Jr,Rybicki FJ.Medical 3D printing cost-savings in orthopedic and maxillofacial surgery: cost analysis of operating room time saved with 3D printed anatomic models and surgical guides.Acad Radiol2020;27:1103-13 PMCID:PMC7078060

[26] Ravi P,Farahani S.University of Cincinnati 3D Printing Clinical Service ParticipantsUtility and costs during the initial year of 3D printing in an academic hospital.J Am Coll Radiol2023;20:193-204

[27] Cifuentes IJ,Salisbury MC,Ortega C.Augmented reality and dynamic infrared thermography for perforator mapping in the anterolateral thigh.Arch Plast Surg2018;45:284-8 PMCID:PMC5968320

[28] Hudson T,Mullner D,Scomacao I.The utility of smartphone-based thermal imaging in the management and monitoring of microvascular flap procedures: a systematic review and meta-analysis.Ann Plast Surg2023;90:S420-5

[29] Huang RW,Hsieh YH.Reliability of postoperative free flap monitoring with a novel prediction model based on supervised machine learning.Plast Reconstr Surg2023;152:943e-52e

[30] Hsu SY,Huang RW.Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study.Int J Surg2023;109:1584-93

[31] Kim J,Kim DE.Development of an automated free flap monitoring system based on artificial intelligence.JAMA Netw Open2024;7:e2424299

[32] Lee C,Wang H,Perng C.Utilizing mask RCNN for monitoring postoperative free flap: circulatory compromise detection based on visible-light and infrared images.IEEE Access2022;10:109510-25

[33] Ramkumar PN,Bloomfield MR.Artificial intelligence and arthroplasty at a single institution: real-world applications of machine learning to big data, value-based care, mobile health, and remote patient monitoring.J Arthroplasty2019;34:2204-9

[34] Shi YC,Li SJ.Flap failure prediction in microvascular tissue reconstruction using machine learning algorithms.World J Clin Cases2022;10:3729-38 PMCID:PMC9100718

[35] Wang SY,Ng JJ.Predicting reoperation and readmission for head and neck free flap patients using machine learning.Head Neck2024;46:1999-2009

[36] Hassan AM,Asaad M.Artificial intelligence modeling to predict periprosthetic infection and explantation following implant-based reconstruction.Plast Reconstr Surg2023;152:929-38

[37] Braun SE,Meyer AM,Butterworth JA.Predicting complications in breast reconstruction: development and prospective validation of a machine learning model.Ann Plast Surg2023;91:282-6

[38] Plastic surgery statistics. American society of plastic surgeons. Available from: https://www.plasticsurgery.org/plastic-surgery-statistics. [Last accessed on 30 Dec 2024]

[39] Masoomi H,Clemens MW.Autologous breast reconstruction trends in the United States: using the nationwide inpatient sample database.Ann Plast Surg2021;87:242-7

[40] Fracol M,Selimos B.Pushing the DIEP envelope with ERAS: 24 hour discharge is safe in appropriately selected patients.Plast Reconstr Surg Glob Open2023;11:e5070 PMCID:PMC10313246

[41] Sauro KM,Ibadin S.Enhanced recovery after surgery guidelines and hospital length of stay, readmission, complications, and mortality: a meta-analysis of randomized clinical trials.JAMA Netw Open2024;7:e2417310 PMCID:PMC11195621

[42] Vecchio D, Stein MJ, Dayan E, Marte J, Theodorou S. Nanotechnology and artificial intelligence: an emerging paradigm for postoperative patient care.Aesthet Surg J2023;43:748-57

[43] Pfob A,Nelson JA,Pusic AL.Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001).Breast2021;60:111-22 PMCID:PMC8551470

[44] Davis L. Northwell releases AI-driven pregnancy chatbot | Northwell Health. January 11, 2023. Available from: https://www.northwell.edu/news/the-latest/northwell-releases-ai-driven-pregnancy-chatbot. [Last accessed on 30 Dec 2024]

[45] Vallurupalli M,Vyas RM.Optimizing readability of patient-facing hand surgery education materials using Chat Generative Pretrained Transformer (ChatGPT) 3.5.J Hand Surg Am2024;49:986-91

[46] FDA news release. FDA strengthens safety requirements and updates study results for breast implants. FDA. October 28, 2021. Available from: https://www.fda.gov/news-events/press-announcements/fda-strengthens-safety-requirements-and-updates-study-results-breast-implants. [Last accessed on 30 Dec 2024]

[47] Cohen WA,Hamill JB.Understanding and optimizing the patient experience in breast reconstruction.Ann Plast Surg2016;77:237-41 PMCID:PMC4685029

[48] Vargas CR,Chuang DJ,Lee BT.Online patient resources for breast reconstruction: an analysis of readability.Plast Reconstr Surg2014;134:406-13

[49] Farid Y,Ortiz S.Artificial intelligence in plastic surgery: insights from plastic surgeons, education integration, ChatGPT's survey predictions, and the path forward.Plast Reconstr Surg Glob Open2024;12:e5515

[50] Jüni P,Egger M.Systematic reviews in health care: assessing the quality of controlled clinical trials.BMJ2001;323:42-6 PMCID:PMC1120670

[51] Hopewell S,Clarke MJ,Dickersin K.Publication bias in clinical trials due to statistical significance or direction of trial results.Cochrane Database Syst Rev2009;2009:MR000006