Camera-based assessment of cutaneous perfusion strength in a clinical setting

Investigation on the influence of the skin tone on hyperspectral imaging for free flap surgery

Hyperspectral imaging (HSI) is a new emerging modality useful for the noncontact assessment of free flap perfusion. This measurement technique relies on the optical properties within the tissue. Since the optical properties of hemoglobin (Hb) and melanin overlap, the results of the perfusion assessment and other tissue-specific parameters are likely to be distorted by the melanin, especially at higher melanin concentrations. Many spectroscopic devices have been shown to struggle with a melanin related bias, which results in a clinical need to improve non-invasive perfusion assessment, especially for a more pigmented population. This study investigated the influence of skin tones on tissue indices measurements using HSI. In addition, other factors that might affect HSI, such as age, body mass index (BMI), sex or smoking habits, were also considered. Therefore, a prospective feasibility study was conducted, including 101 volunteers from whom tissue indices measurements were performed on 16 different body sites. Skin tone classification was performed using the Fitzpatrick skin type classification questionnaire, and the individual typology angle (ITA) acquired from the RGB images was calculated simultaneously with the measurements. Tissue indices provided by the used HSI-device were correlated to the possible influencing factors. The results show that a dark skin tone and, therefore, higher levels of pigmentation influence the HSI-derived tissue indices. In addition, possible physiological factors influencing the HSI-measurements were found. In conclusion, the HSI-based tissue indices can be used for perfusion assessment for people with lighter skin tone levels but show limitations in people with darker skin tones. Furthermore, it could be used for a more individual perfusion assessment if different physiological influencing factors are respected.

An overview of current assessment techniques for evaluating cutaneous perfusion in reconstructive surgery

This article provides a comprehensive analysis of modern techniques used in the assessment of cutaneous flaps in reconstructive surgery. It emphasizes the importance of preoperative planning and intra- and perioperative assessment of flap perfusion to ensure successful outcomes. Despite technological advancements, direct clinical assessment remains the gold standard. We categorized assessment techniques into non-invasive and invasive modalities, discussing their strengths and weaknesses. Non-invasive methods, such as acoustic Doppler sonography, near-infrared spectroscopy, hyperspectral imaging thermal imaging, and remote-photoplethysmography, offer accessibility and safety but may sacrifice specificity. Invasive techniques, including contrast-enhanced ultrasound, computed tomography angiography, near-infrared fluorescence angiography with indocyanine green, and implantable Doppler probe, provide high accuracy but introduce additional risks. We emphasize the need for a tailored decision-making process based on specific clinical scenarios, patient characteristics, procedural requirements, and surgeon expertise. It also discusses potential future advancements in flap assessment, including the integration of artificial intelligence and emerging technologies.

Continuous intraoperative perfusion monitoring of free microvascular anastomosed fasciocutaneous flaps using remote photoplethysmography

Flap loss through limited perfusion remains a major complication in reconstructive surgery. Continuous monitoring of perfusion will facilitate early detection of insufficient perfusion. Remote or imaging photoplethysmography (rPPG/iPPG) as a non-contact, non-ionizing, and non-invasive monitoring technique provides objective and reproducible information on physiological parameters. The aim of this study is to establish rPPG for intra- and postoperative monitoring of flap perfusion in patients undergoing reconstruction with free fasciocutaneous flaps (FFCF). We developed a monitoring algorithm for flap perfusion, which was evaluated in 15 patients. For 14 patients, ischemia of the FFCF in the forearm and successful reperfusion of the implanted FFCF was quantified based on the local signal. One FFCF showed no perfusion after reperfusion and devitalized in the course. Intraoperative monitoring of perfusion with rPPG provides objective and reproducible results. Therefore, rPPG is a promising technology for standard flap perfusion monitoring on low costs without the need for additional monitoring devices.

Deep learning-based remote-photoplethysmography measurement from short-time facial video

Objective. Efficient non-contact heart rate (HR) measurement from facial video has received much attention in health monitoring. Past methods relied on prior knowledge and an unproven hypothesis to extract remote photoplethysmography (rPPG) signals, e.g. manually designed regions of interest (ROIs) and the skin reflection model.Approach. This paper presents a short-time end to end HR estimation framework based on facial features and temporal relationships of video frames. In the proposed method, a deep 3D multi-scale network with cross-layer residual structure is designed to construct an autoencoder and extract robust rPPG features. Then, a spatial-temporal fusion mechanism is proposed to help the network focus on features related to rPPG signals. Both shallow and fused 3D spatial-temporal features are distilled to suppress redundant information in the complex environment. Finally, a data augmentation strategy is presented to solve the problem of uneven distribution of HR in existing datasets.Main results. The experimental results on four face-rPPG datasets show that our method overperforms the state-of-the-art methods and requires fewer video frames. Compared with the previous best results, the proposed method improves the root mean square error (RMSE) by 5.9%, 3.4% and 21.4% on the OBF dataset (intra-test), COHFACE dataset (intra-test) and UBFC dataset (cross-test), respectively.Significance. Our method achieves good results on diverse datasets (i.e. highly compressed video, low-resolution and illumination variation), demonstrating that our method can extract stable rPPG signals in short time.