Automatic detection of perforators for microsurgical reconstruction

Abstract

a b s t r a c tThe deep inferior epigastric perforator (DIEP) is the most commonly used free flap in mastectomy reconstruction.Preoperative imaging techniques are routinely used to detect location, diameter and course of perforators, with direct intervention from the imaging team, who subsequently draw a chart that will help surgeons choosing the best vascular support for the reconstruction.In this work, the feasibility of using a computer software to support the preoperative planning of 40 patients proposed for breast reconstruction with a DIEP flap is evaluated for the first time. Blood vessel centreline extraction and local characterization algorithms are applied to identify perforators and compared with the manual mapping, aiming to reduce the time spent by the imaging team, as well as the inherent subjectivity to the task.Comparing with the measures taken during surgery, the software calibre estimates were worse for vessels smaller than 1.5 mm (P ¼ 6e-4) but better for the remaining ones (P ¼ 2e-3). Regarding vessel location, the vertical component of the software output was significantly different from the manual measure (P ¼ 0.02), nonetheless that was irrelevant during surgery as errors in the order of 2e3 mm do not have impact in the dissection step.Our trials support that a reduction of the time spent is achievable using the automatic tool (about 2 h/ case).The introduction of artificial intelligence in clinical practice intends to simplify the work of health professionals and to provide better outcomes to patients. This pilot study paves the way for a success story.

Imaging for thinned perforator flap harvest: current status and future perspectives

“…Machine learning, a subfield of AI, seems to be the best approach for processing the vast amount of data collected. Acknowledging its efficiency in donor site-related complication prediction [ 69 ], automatic perforator detection [ 70 ] and postoperative monitoring [ 71 ] in microsurgical reconstruction, we foresee the possibility of machine learning enhancing the success rate in thinned perforator flaps through the abovementioned perspectives.…”

Artificial intelligence (AI) in breast cancer care - Leveraging multidisciplinary skills to improve care

“…The capacity to automatically detect vessels from angio CTs, proposed by Mavioso et al. will hopefully replace the process of manual annotation by a computerized automatic analysis keeping the same accuracy levels [ 11 ].…”

“…Some work has also been done in breast reconstruction through image analysis of angio CTs, the routine exam used to plan free flap reconstruction after mastectomy. The capacity to automatically detect vessels from angio CTs, proposed by Mavioso et al will hopefully replace the process of manual annotation by a computerized automatic analysis keeping the same accuracy levels [11].…”

Automatic detection of perforators for microsurgical reconstruction

“…The current gold standard for perforator selection is CTA but it has disadvantages: use of intravenous (IV) contrast agents and ionizing radiation, high purchasing costs, a lack of perioperative usability, and a lack of physiological information on flow characteristics [ 3 ]. The use of AI will improve reproducibility and decrease time-consuming human input in perforator selection [ 1 ]. Dynamic Infrared Thermography (DIRT) can be an alternative technique in perforator mapping and perfusion assesment [ 4 , 5 ].…”

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We read with interest the article by Mavioso et al this is a valuable pilot study on the use of artificial intelligence (AI) in preoperative mapping of perforators with computed tomography (CTA) [1]. Selection of the best perforators is of uttermost importance, as it is the only source of blood supply to the flap.

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