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The usefulness of artificial intelligence in breast reconstruction: a systematic review

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Abstract

Background

Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to prevent negative results. This systematic review aimed to evaluate the usefulness of AI in breast reconstruction.

Methods

A systematic review was conducted in August 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, EMBASE, SCOPUS, and Google Scholar online databases were queried to capture all publications studying the use of artificial intelligence in breast reconstruction.

Results

A total of 23 studies were full text-screened after removing duplicates, and twelve articles fulfilled our inclusion criteria. The Machine Learning algorithms applied for neuropathic pain, lymphedema diagnosis, microvascular abdominal flap failure, donor site complications associated to muscle sparing Transverse Rectus Abdominis flap, surgical complications, financial toxicity, and patient-reported outcomes after breast surgery demonstrated that AI is a helpful tool to accurately predict patient results. In addition, one study used Computer Vision technology to assist in Deep Inferior Epigastric Perforator Artery detection for flap design, considerably reducing the preoperative time compared to manual identification.

Conclusions

In breast reconstruction, AI can help the surgeon by optimizing the perioperative patients’ counseling to predict negative outcomes, allowing execution of timely interventions and reducing the postoperative burden, which leads to obtaining the most successful results and improving patient satisfaction.

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Acknowledgements

None.

Funding

This work was funded by Mayo Clinic Clinical Research Operations Group, Mayo Clinic Center for Regenerative Medicine.

Author information

Authors and Affiliations

  1. Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA

    Karla C. Maita, Francisco R. Avila, Ricardo A. Torres-Guzman, John P. Garcia, Gioacchino D. De Sario Velasquez, Sahar Borna, Olivia S. Ho & Antonio Jorge Forte

  2. Department of Administration, Mayo Clinic, Jacksonville, FL, USA

    Sally A. Brown

  3. Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA

    Clifton R. Haider

Authors
  1. Karla C. Maita
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  2. Francisco R. Avila
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  3. Ricardo A. Torres-Guzman
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  4. John P. Garcia
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  5. Gioacchino D. De Sario Velasquez
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  6. Sahar Borna
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  7. Sally A. Brown
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  8. Clifton R. Haider
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  9. Olivia S. Ho
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  10. Antonio Jorge Forte
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Contributions

All authors contributed to the study conception and design. They have accepted responsibility for the entire content of this manuscript and approved its submission. Francisco R Avila, and Ricardo A Torres-Guzman, John P Garcia, Gioacchino D De Sario Velasquez, and Sahar Borna conducted the data collection and studies selection. Karla C Maita analyzed the results and wrote the first draft of the article. Olivia S Ho, Sally A Brown, Clifton R Haider, and Antonio Jorge Forte performed a critical revision of the manuscript. Finally, all the authors approved the last version for publication.

Corresponding author

Correspondence to Antonio Jorge Forte.

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Maita, K.C., Avila, F.R., Torres-Guzman, R.A. et al. The usefulness of artificial intelligence in breast reconstruction: a systematic review. Breast Cancer (2024). https://doi.org/10.1007/s12282-024-01582-6

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