Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

Photoplethysmography-Based Angiography of Skin Tumors in Arbitrary Areas of Human Body

Noninvasive, rapid, and robust diagnostic techniques for clinical screening of tumors located in arbitrary areas of the human body are in demand. To address this challenge, we analyzed the feasibility of photoplethysmography-based angiography for assessing vascular structures within malignant and benign tumors. The proposed hardware and software were approved in a clinical study involving 30 patients with tumors located in the legs, torso, arms, and head. High-contrast and detailed vessel maps within both benign and malignant tumors were obtained. We demonstrated that capillary maps are consistent and can be interpreted using well-established dermoscopic criteria for vascular morphology. Vessel mapping provides valuable details, which may not be available in dermoscopic images and can aid in determining whether a tumor is benign or malignant. We believe that the proposed approach may become a valuable tool in the preliminary cancer diagnosis and is suitable for large-scale screening.

Non-invasive estimation of the powder size distribution from a single speckle image

Non-invasive characterization of powders may take one of two approaches: imaging and counting individual particles; or relying on scattered light to estimate the particle size distribution (PSD) of the ensemble. The former approach runs into practical difficulties, as the system must conform to the working distance and other restrictions of the imaging optics. The latter approach requires an inverse map from the speckle autocorrelation to the particle sizes. The principle relies on the pupil function determining the basic sidelobe shape, whereas the particle size spread modulates the sidelobe intensity. We recently showed that it is feasible to invert the speckle autocorrelation and obtain the PSD using a neural network, trained efficiently through a physics-informed semi-generative approach. In this work, we eliminate one of the most time-consuming steps of our previous method by engineering the pupil function. By judiciously blocking portions of the pupil, we sacrifice some photons but in return we achieve much enhanced sidelobes and, hence, higher sensitivity to the change of the size distribution. The result is a 60 × reduction in total acquisition and processing time, or 0.25 seconds per frame in our implementation. Almost real-time operation in our system is not only more appealing toward rapid industrial adoption, it also paves the way for quantitative characterization of complex spatial or temporal dynamics in drying, blending, and other chemical and pharmaceutical manufacturing processes.

Classification of melanocytic lesions using direct illumination multispectral imaging

With rising melanoma incidence and mortality, early detection and surgical removal of primary lesions is essential. Multispectral imaging is a new, non-invasive technique that can facilitate skin cancer detection by measuring the reflectance spectra of biological tissues. Currently, incident illumination allows little light to be reflected from deeper skin layers due to high surface reflectance. A pilot study was conducted at the University Hospital Basel to evaluate, whether multispectral imaging with direct light coupling could extract more information from deeper skin layers for more accurate dignity classification of melanocytic lesions. 27 suspicious pigmented lesions from 23 patients were included (6 melanomas, 6 dysplastic nevi, 12 melanocytic nevi, 3 other). Lesions were imaged before excision using a prototype snapshot mosaic multispectral camera with incident and direct illumination with subsequent dignity classification by a pre-trained multispectral image analysis model. Using incident light, a sensitivity of 83.3% and a specificity of 58.8% were achieved compared to dignity as determined by histopathological examination. Direct light coupling resulted in a superior sensitivity of 100% and specificity of 82.4%. Convolutional neural network classification of corresponding red, green, and blue lesion images resulted in 16.7% lower sensitivity (83.3%, 5/6 malignant lesions detected) and 20.9% lower specificity (61.5%) compared to direct light coupling with multispectral image classification. Our results show that incorporating direct light multispectral imaging into the melanoma detection process could potentially increase the accuracy of dignity classification. This newly evaluated illumination method could improve multispectral applications in skin cancer detection. Further larger studies are needed to validate the camera prototype.

Skin-CAD: Explainable deep learning classification of skin cancer from dermoscopic images by feature selection of dual high-level CNNs features and transfer learning

Skin cancer (SC) significantly impacts many individuals' health all over the globe. Hence, it is imperative to promptly identify and diagnose such conditions at their earliest stages using dermoscopic imaging. Computer-aided diagnosis (CAD) methods relying on deep learning techniques especially convolutional neural networks (CNN) can effectively address this issue with outstanding outcomes. Nevertheless, such black box methodologies lead to a deficiency in confidence as dermatologists are incapable of comprehending and verifying the predictions that were made by these models. This article presents an advanced an explainable artificial intelligence (XAI) based CAD system named "Skin-CAD" which is utilized for the classification of dermoscopic photographs of SC. The system accurately categorises the photographs into two categories: benign or malignant, and further classifies them into seven subclasses of SC. Skin-CAD employs four CNNs of different topologies and deep layers. It gathers features out of a pair of deep layers of every CNN, particularly the final pooling and fully connected layers, rather than merely depending on attributes from a single deep layer. Skin-CAD applies the principal component analysis (PCA) dimensionality reduction approach to minimise the dimensions of pooling layer features. This also reduces the complexity of the training procedure compared to using deep features from a CNN that has a substantial size. Furthermore, it combines the reduced pooling features with the fully connected features of each CNN. Additionally, Skin-CAD integrates the dual-layer features of the four CNNs instead of entirely depending on the features of a single CNN architecture. In the end, it utilizes a feature selection step to determine the most important deep attributes. This helps to decrease the general size of the feature set and streamline the classification process. Predictions are analysed in more depth using the local interpretable model-agnostic explanations (LIME) approach. This method is used to create visual interpretations that align with an already existing viewpoint and adhere to recommended standards for general clarifications. Two benchmark datasets are employed to validate the efficiency of Skin-CAD which are the Skin Cancer: Malignant vs. Benign and HAM10000 datasets. The maximum accuracy achieved using Skin-CAD is 97.2 % and 96.5 % for the Skin Cancer: Malignant vs. Benign and HAM10000 datasets respectively. The findings of Skin-CAD demonstrate its potential to assist professional dermatologists in detecting and classifying SC precisely and quickly.

Pixel-level classification of pigmented skin cancer lesions using multispectral autofluorescence lifetime dermoscopy imaging

There is no clinical tool available to primary care physicians or dermatologists that could provide objective identification of suspicious skin cancer lesions. Multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy enables label-free biochemical and metabolic imaging of skin lesions. This study investigated the use of pixel-level maFLIM dermoscopy features for objective discrimination of malignant from visually similar benign pigmented skin lesions. Clinical maFLIM dermoscopy images were acquired from 60 pigmented skin lesions before undergoing a biopsy examination. Random forest and deep neural networks classification models were explored, as they do not require explicit feature selection. Feature pools with either spectral intensity or bi-exponential maFLIM features, and a combined feature pool, were independently evaluated with each classification model. A rigorous cross-validation strategy tailored for small-size datasets was adopted to estimate classification performance. Time-resolved bi-exponential autofluorescence features were found to be critical for accurate detection of malignant pigmented skin lesions. The deep neural network model produced the best lesion-level classification, with sensitivity and specificity of 76.84%±12.49% and 78.29%±5.50%, respectively, while the random forest classifier produced sensitivity and specificity of 74.73%±14.66% and 76.83%±9.58%, respectively. Results from this study indicate that machine-learning driven maFLIM dermoscopy has the potential to assist doctors with identifying patients in real need of biopsy examination, thus facilitating early detection while reducing the rate of unnecessary biopsies.

Complex Spatial Illumination Scheme Optimization of Backscattering Mueller Matrix Polarimetry for Tissue Imaging and Biosensing

Polarization imaging and sensing techniques have shown great potential for biomedical and clinical applications. As a novel optical biosensing technology, Mueller matrix polarimetry can provide abundant microstructural information of tissue samples. However, polarimetric aberrations, which lead to inaccurate characterization of polarization properties, can be induced by uneven biomedical sample surfaces while measuring Mueller matrices with complex spatial illuminations. In this study, we analyze the detailed features of complex spatial illumination-induced aberrations by measuring the backscattering Mueller matrices of experimental phantom and tissue samples. We obtain the aberrations under different spatial illumination schemes in Mueller matrix imaging. Furthermore, we give the corresponding suggestions for selecting appropriate illumination schemes to extract specific polarization properties, and then provide strategies to alleviate polarimetric aberrations by adjusting the incident and detection angles in Mueller matrix imaging. The optimized scheme gives critical criteria for the spatial illumination scheme selection of non-collinear backscattering Mueller matrix measurements, which can be helpful for the further development of quantitative tissue polarimetric imaging and biosensing.

Towards label-free non-invasive autofluorescence multispectral imaging for melanoma diagnosis

This study focuses on the use of cellular autofluorescence which visualizes the cell metabolism by monitoring endogenous fluorophores including NAD(P)H and flavins. It explores the potential of multispectral imaging of native fluorophores in melanoma diagnostics using excitation wavelengths ranging from 340 nm to 510 nm and emission wavelengths above 391 nm. Cultured immortalized cells are utilized to compare the autofluorescent signatures of two melanoma cell lines to one fibroblast cell line. Feature analysis identifies the most significant and least correlated features for differentiating the cells. The investigation successfully applies this analysis to pre-processed, noise-removed images and original background-corrupted data. Furthermore, the applicability of distinguishing melanomas and healthy fibroblasts based on their autofluorescent characteristics is validated using the same evaluation technique on patient cells. Additionally, the study tentatively maps the detected features to underlying biological processes. This research demonstrates the potential of cellular autofluorescence as a promising tool for melanoma diagnostics.

Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis.

Acceleration of Hyperspectral Skin Cancer Image Classification through Parallel Machine-Learning Methods

Hyperspectral imaging (HSI) has become a very compelling technique in different scientific areas; indeed, many researchers use it in the fields of remote sensing, agriculture, forensics, and medicine. In the latter, HSI plays a crucial role as a diagnostic support and for surgery guidance. However, the computational effort in elaborating hyperspectral data is not trivial. Furthermore, the demand for detecting diseases in a short time is undeniable. In this paper, we take up this challenge by parallelizing three machine-learning methods among those that are the most intensively used: Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB) algorithms using the Compute Unified Device Architecture (CUDA) to accelerate the classification of hyperspectral skin cancer images. They all showed a good performance in HS image classification, in particular when the size of the dataset is limited, as demonstrated in the literature. We illustrate the parallelization techniques adopted for each approach, highlighting the suitability of Graphical Processing Units (GPUs) to this aim. Experimental results show that parallel SVM and XGB algorithms significantly improve the classification times in comparison with their serial counterparts.

Line-field confocal optical coherence tomography for in vivo visualization of morphological characteristics of squamous cell carcinoma in a murine model

OBJECTIVES

Non-invasive imaging with line-field confocal optical coherence tomography (LC-OCT) can support the diagnosis of squamous cell carcinoma (SCC) through visualization of morphological characteristics specific to skin cancer. We aimed to visualize prominent morphological characteristics of SCC using LC-OCT in a well-established murine SCC model.

MATERIALS AND METHODS

Nine hairless mice were exposed to ultraviolet radiation three times weekly for 9 months to induce SCC development. Visible SCC tumors (n = 9) were imaged with LC-OCT and the presence of 10 well-described morphological characteristics of SCC were evaluated in the scans by two physicians with adjudication by a third.

RESULTS

Overall, murine morphological characteristics resembled corresponding features previously reported in human SCCs. Interrupted dermal-epidermal junction occurred in 100% of tumors. In epidermis, the most frequently observed characteristics were severe epidermal dysplasia (100%) and tumor budding (89%). Common dermal characteristics included broad strands (100%) and collagen alterations (78%).

CONCLUSION

LC-OCT imaging can be used to non-invasively visualize morphological characteristics specific to SCC in an in vivo preclinical model.

Optical imaging technologies for in vivo cancer detection in low-resource settings

Cancer continues to affect underserved populations disproportionately. Novel optical imaging technologies, which can provide rapid, non-invasive, and accurate cancer detection at the point of care, have great potential to improve global cancer care. This article reviews the recent technical innovations and clinical translation of low-cost optical imaging technologies, highlighting the advances in both hardware and software, especially the integration of artificial intelligence, to improve in vivo cancer detection in low-resource settings. Additionally, this article provides an overview of existing challenges and future perspectives of adapting optical imaging technologies into clinical practice, which can potentially contribute to novel insights and programs that effectively improve cancer detection in low-resource settings.

A Review of Recent Advances in Computer-Aided Detection Methods Using Hyperspectral Imaging Engineering to Detect Skin Cancer

Skin cancer, a malignant neoplasm originating from skin cell types including keratinocytes, melanocytes, and sweat glands, comprises three primary forms: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and malignant melanoma (MM). BCC and SCC, while constituting the most prevalent categories of skin cancer, are generally considered less aggressive compared to MM. Notably, MM possesses a greater capacity for invasiveness, enabling infiltration into adjacent tissues and dissemination via both the circulatory and lymphatic systems. Risk factors associated with skin cancer encompass ultraviolet (UV) radiation exposure, fair skin complexion, a history of sunburn incidents, genetic predisposition, immunosuppressive conditions, and exposure to environmental carcinogens. Early detection of skin cancer is of paramount importance to optimize treatment outcomes and preclude the progression of disease, either locally or to distant sites. In pursuit of this objective, numerous computer-aided diagnosis (CAD) systems have been developed. Hyperspectral imaging (HSI), distinguished by its capacity to capture information spanning the electromagnetic spectrum, surpasses conventional RGB imaging, which relies solely on three color channels. Consequently, this study offers a comprehensive exploration of recent CAD investigations pertaining to skin cancer detection and diagnosis utilizing HSI, emphasizing diagnostic performance parameters such as sensitivity and specificity.

Polarimetric imaging combining optical parameters for classification of mice non-melanoma skin cancer tissue using machine learning

Polarimetric imaging systems combining machine learning is emerging as a promising tool for the support of diagnosis and intervention decision-making processes in cancer detection/staging. A present study proposes a novel method based on Mueller matrix imaging combining optical parameters and machine learning models for classifying the progression of skin cancer based on the identification of three different types of mice skin tissues: healthy, papilloma, and squamous cell carcinoma. Three different machine learning algorithms (K-Nearest Neighbors, Decision Tree, and Support Vector Machine (SVM)) are used to construct a classification model using a dataset consisting of Mueller matrix images and optical properties extracted from the tissue samples. The experimental results show that the SVM model is robust to discriminate among three classes in the training stage and achieves an accuracy of 94 % on the testing dataset. Overall, it is provided that polarimetric imaging systems and machine learning algorithms can dynamically combine for the reliable diagnosis of skin cancer.

Optical imaging for early detection of cervical cancer: state of the art and perspectives

Significance

Cervical cancer is one of the major causes of death in females worldwide. HPV infection is the key cause of uncontrolled cell growth leading to cervical cancer. About 90% of cervical cancer is preventable because of the slow progression of the disease, giving a window of about 10 years for the precancerous lesion to be recognized and treated.

Aim

The present challenges for cervical cancer diagnosis are interobserver variation in clinicians' interpretation of visual inspection with acetic acid/visual inspection with Lugol's iodine, cost of cytology-based screening, and lack of skilled clinicians. The optical modalities can assist in qualitatively and quantitatively analyzing the tissue to differentiate between cancerous and surrounding normal tissues.

Approach

This work is on the recent advances in optical techniques for cervical cancer diagnosis, which promise to overcome the above-listed challenges faced by present screening techniques.

Results

The optical modalities provide substantial measurable information in addition to the conventional colposcopy and Pap smear test to clinically aid the diagnosis.

Conclusions

Recent optical modalities on fluorescence, multispectral imaging, polarization-sensitive imaging, microendoscopy, Raman spectroscopy, especially with the portable design and assisted by artificial intelligence, have a significant scope in the diagnosis of premalignant cervical cancer in future.

Surface-enhanced Raman scattering (SERS) by gold nanoparticle characterizes dermal thickening by collagen in bleomycin-treated skin ex vivo

PURPOSE

Current skin imaging modalities, including optical, electron, and confocal microscopy, mostly require tissue fixations that could damage proteins and biological molecules. Live tissue or cell imaging such as ultrasonography and optical coherent microscope may not adequately measure the dynamic spectroscopical changes. Raman spectroscopy has been adopted for skin imaging in vivo, mostly for skin cancer imaging. However, whether the epidermal and dermal thickening in skin could be measured and distinguished by conventional Ramen spectroscopy or the surface-enhanced Raman scattering (SERS), a rapid and label-free method for noninvasive measurement remains unknown.

METHODS

Human skin sections from patients of atopic dermatitis and keloid, which represent epidermal and dermal thickening, respectively, were measured by conventional Ramen spectroscopy. In mice, skin sections from imiquimod (IMQ)- and bleomycin (BLE)-treated mice, which reflect the epidermal and dermal thickening, respectively, were measured by SERS, that incorporates gold nanoparticles to generate surface plasma and enhance Raman signals.

RESULTS

Conventional Ramen spectroscopy failed to consistently show the Raman shift in human samples among the different groups. SERS successfully revealed a prominent peak around 1300 cm-1 in the IMQ-treated skin; and two significant peaks around 1100 and 1300 cm-1 in BLE-treated group. Further quantitative analysis showed 1100 cm-1 peak was significantly accentuated in the BLE-treated skin than that in control skin. SERS identified in vitro a similar 1100 cm-1 peak in solutions of collagen, the major dermal biological molecules.

CONCLUSION

SERS distinguishes the epidermal or dermal thickening in mouse skin with rapid and label-free measures. A prominent 1100 cm-1 SERS peak in the BLE-treated skin may result from collagen. SERS might help precision diagnosis in the future.

Multispectral Imaging for Skin Diseases Assessment-State of the Art and Perspectives

Skin optical inspection is an imperative procedure for a suspicious dermal lesion since very early skin cancer detection can guarantee total recovery. Dermoscopy, confocal laser scanning microscopy, optical coherence tomography, multispectral imaging, multiphoton laser imaging, and 3D topography are the most outstanding optical techniques implemented for skin examination. The accuracy of dermatological diagnoses attained by each of those methods is still debatable, and only dermoscopy is frequently used by all dermatologists. Therefore, a comprehensive method for skin analysis has not yet been established. Multispectral imaging (MSI) is based on light-tissue interaction properties due to radiation wavelength variation. An MSI device collects the reflected radiation after illumination of the lesion with light of different wavelengths and provides a set of spectral images. The concentration maps of the main light-absorbing molecules in the skin, the chromophores, can be retrieved using the intensity values from those images, sometimes even for deeper-located tissues, due to interaction with near-infrared light. Recent studies have shown that portable and cost-efficient MSI systems can be used for extracting skin lesion characteristics useful for early melanoma diagnoses. This review aims to describe the efforts that have been made to develop MSI systems for skin lesions evaluation in the last decade. We examined the hardware characteristics of the produced devices and identified the typical structure of an MSI device for dermatology. The analyzed prototypes showed the possibility of improving the specificity of classification between the melanoma and benign nevi. Currently, however, they are rather adjuvants tools for skin lesion assessment, and efforts are needed towards a fully fledged diagnostic MSI device.

Impact of COVID-19 pandemic on the diagnosis of patients with skin cancer: a systematic review protocol

INTRODUCTION

The COVID-19 pandemic has changed aspects of patient care in the many scheduled medical activities, restricted access to healthcare facilities, and affected the diagnosis and organisation of patients with other health problems, specifically skin cancer. Skin cancer, the uninhibited progress of atypical skin cells, happens with unrepaired DNA genetic faults that lead them to multiply and create malignant tumours. Currently, dermatologists perform skin cancer diagnosis based on their specialised experience using the results of pathological tests from the skin biopsy. Sometimes, some specialists advise sonography imaging to check the skin tissue as a non-invasive method. The outbreak has led to postponements in the treatment and diagnosis of patients with skin cancer, including diagnostic delays because of limitations of diagnostic capacities and delays in referring patients to the physician. The purpose of this review is to improve our understanding of the impact of the COVID-19 outbreak on the diagnosis of patients with skin cancer and conduct a scoping review to identify whether routine skin cancer diagnoses are affected by the persistent incidence of COVID-19.

METHODS AND ANALYSIS

The structure of research was compiled using Population/Intervention/Comparison/Outcomes/Study Design and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, we will find the main keywords to capture scientific studies related to the impact of the COVID-19 pandemic on the diagnosis of skin cancer: COVID-19 and skin neoplasms. To warrant sufficient coverage and identify potential articles, we will search the combination of four electronic databases PubMed/MEDLINE, Scopus, Web of Science and EMBASE, and ProQuest from 1 January 2019 until 30 September 2022. The screening, selection and data extraction of studies will be performed by two independent authors, who will then assessed the quality of the included studies according to Newcastle-Ottawa Scale.

ETHICS AND DISSEMINATION

As this study will be a systematic review without human participants' involvement, no formal ethical assessment is required. Findings will be presented at conferences related to this field and will be disseminated in a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42022361569.

Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)

Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law.

Skin Cancer Pathobiology at a Glance: A Focus on Imaging Techniques and Their Potential for Improved Diagnosis and Surveillance in Clinical Cohorts

Early diagnosis is essential for completely eradicating skin cancer and maximizing patients' clinical benefits. Emerging optical imaging modalities such as reflectance confocal microscopy (RCM), optical coherence tomography (OCT), magnetic resonance imaging (MRI), near-infrared (NIR) bioimaging, positron emission tomography (PET), and their combinations provide non-invasive imaging data that may help in the early detection of cutaneous tumors and surgical planning. Hence, they seem appropriate for observing dynamic processes such as blood flow, immune cell activation, and tumor energy metabolism, which may be relevant for disease evolution. This review discusses the latest technological and methodological advances in imaging techniques that may be applied for skin cancer detection and monitoring. In the first instance, we will describe the principle and prospective clinical applications of the most commonly used imaging techniques, highlighting the challenges and opportunities of their implementation in the clinical setting. We will also highlight how imaging techniques may complement the molecular and histological approaches in sharpening the non-invasive skin characterization, laying the ground for more personalized approaches in skin cancer patients.

Developments and Clinical Applications of Noninvasive Optical Technologies for Skin Cancer Diagnosis

Skin cancer has shown a sharp increase in prevalence over the past few decades and currently accounts for one-third of all cancers diagnosed. The most lethal form of skin cancer is melanoma, which develops in 4% of individuals. The rising prevalence and increased number of fatalities of skin cancer put a significant burden on healthcare resources and the economy. However, early detection and treatment greatly improve survival rates for patients with skin cancer. Since the rising rates of both the incidence and mortality have been particularly noticeable with melanoma, significant resources have been allocated to research aimed at earlier diagnosis and a deeper knowledge of the disease. Dermoscopy, reflectance confocal microscopy, optical coherence tomography, multiphoton-excited fluorescence imaging, and dermatofluorescence are only a few of the optical modalities reviewed here that have been employed to enhance noninvasive diagnosis of skin cancer in recent years. This review article discusses the methodology behind newly emerging noninvasive optical diagnostic technologies, their clinical applications, and advantages and disadvantages of these techniques, as well as the potential for their further advancement in the future.

Self-Mixing Laser Distance-Sensor Enhanced by Multiple Modulation Waveforms

Optical rangefinders based on Self-Mixing Interferometry are widely described in literature, but not yet on the market as commercial instruments. The main reason is that it is relatively easy to propose new elaboration techniques and get results in controlled conditions, while it is very difficult to develop a reliable instrument. In this paper, we propose a laser distance sensor with improved reliability, realized through a wavelength modulation at a different frequency, able to decorrelate single measurement errors and obtain improvement by averages. A dedicated software is implemented to automatically calculate the modulation pre-emphasis, needed to linearize the wavelength modulation. Finally, data selection algorithms allow to overcome signal fading problems due to the speckle effect. A prototype demonstrates the approach with about 0.1 mm accuracy up to 2 m of distance at 200 measurements per second.

Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images

Cancer originates from the uncontrolled growth of healthy cells into a mass. Chromophores, such as hemoglobin and melanin, characterize skin spectral properties, allowing the classification of lesions into different etiologies. Hyperspectral imaging systems gather skin-reflected and transmitted light into several wavelength ranges of the electromagnetic spectrum, enabling potential skin-lesion differentiation through machine learning algorithms. Challenged by data availability and tiny inter and intra-tumoral variability, here we introduce a pipeline based on deep neural networks to diagnose hyperspectral skin cancer images, targeting a handheld device equipped with a low-power graphical processing unit for routine clinical testing. Enhanced by data augmentation, transfer learning, and hyperparameter tuning, the proposed architectures aim to meet and improve the well-known dermatologist-level detection performances concerning both benign-malignant and multiclass classification tasks, being able to diagnose hyperspectral data considering real-time constraints. Experiments show 87% sensitivity and 88% specificity for benign-malignant classification and specificity above 80% for the multiclass scenario. AUC measurements suggest classification performance improvement above 90% with adequate thresholding. Concerning binary segmentation, we measured skin DICE and IOU higher than 90%. We estimated 1.21 s, at most, consuming 5 Watts to segment the epidermal lesions with the U-Net++ architecture, meeting the imposed time limit. Hence, we can diagnose hyperspectral epidermal data assuming real-time constraints.

Overview of Biofluids and Flow Sensing Techniques Applied in Clinical Practice

This review summarizes the current knowledge on biofluids and the main flow sensing techniques applied in healthcare today. Since the very beginning of the history of medicine, one of the most important assets for evaluating various human diseases has been the analysis of the conditions of the biofluids within the human body. Hence, extensive research on sensors intended to evaluate the flow of many of these fluids in different tissues and organs has been published and, indeed, continues to be published very frequently. The purpose of this review is to provide researchers interested in venturing into biofluid flow sensing with a concise description of the physiological characteristics of the most important body fluids that are likely to be altered by diverse medical conditions. Similarly, a reported compilation of well-established sensors and techniques currently applied in healthcare regarding flow sensing is aimed at serving as a starting point for understanding the theoretical principles involved in the existing methodologies, allowing researchers to determine the most suitable approach to adopt according to their own objectives in this broad field.

FPI Based Hyperspectral Imager for the Complex Surfaces—Calibration, Illumination and Applications

Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pérot interferometer (FPI) based hyperspectral imager, a specially designed LED module and several sizes of stray light protection cones for reaching and adapting to the complex skin surfaces. The imager is designed for the needs of photometric stereo imaging for providing the skin surface models (3D) for each captured wavelength. The captured HS images contained 33 selected wavelengths (ranging from 477 nm to 891 nm), which were captured simultaneously with accordingly selected LEDs and three specific angles of light. The pre-test results show that the data collected with the new SICSURFIS imager enable the use of the spectral and spatial domains with surface model information. The imager can reach complex skin surfaces. Healthy skin, basal cell carcinomas and intradermal nevi lesions were classified and delineated pixel-wise with promising results, but further studies are needed. The results were obtained with a convolutional neural network.

Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours-A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks

Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. In this second part of a three-phase pilot study, we used a novel hand-held SICSURFIS Spectral Imager with an adaptable field of view and target-wise selectable wavelength channels to provide detailed spectral and spatial data for lesions on complex surfaces. The hyperspectral images (33 wavelengths, 477-891 nm) provided photometric data through individually controlled illumination modules, enabling convolutional networks to utilise spectral, spatial, and skin-surface models for the analyses. In total, 42 lesions were studied: 7 melanomas, 13 pigmented and 7 intradermal nevi, 10 basal cell carcinomas, and 5 squamous cell carcinomas. All lesions were excised for histological analyses. A pixel-wise analysis provided map-like images and classified pigmented lesions with a sensitivity of 87% and a specificity of 93%, and 79% and 91%, respectively, for non-pigmented lesions. A majority voting analysis, which provided the most probable lesion diagnosis, diagnosed 41 of 42 lesions correctly. This pilot study indicates that our non-invasive hyperspectral imaging system, which involves shape and depth data analysed by convolutional neural networks, is feasible for differentiating between malignant and benign pigmented and non-pigmented skin tumours, even on complex skin surfaces.

Multiclass Skin Lesion Classification Using a Novel Lightweight Deep Learning Framework for Smart Healthcare

Skin lesion classification has recently attracted significant attention. Regularly, physicians take much time to analyze the skin lesions because of the high similarity between these skin lesions. An automated classification system using deep learning can assist physicians in detecting the skin lesion type and enhance the patient’s health. The skin lesion classification has become a hot research area with the evolution of deep learning architecture. In this study, we propose a novel method using a new segmentation approach and wide-ShuffleNet for skin lesion classification. First, we calculate the entropy-based weighting and first-order cumulative moment (EW-FCM) of the skin image. These values are used to separate the lesion from the background. Then, we input the segmentation result into a new deep learning structure wide-ShuffleNet and determine the skin lesion type. We evaluated the proposed method on two large datasets: HAM10000 and ISIC2019. Based on our numerical results, EW-FCM and wide-ShuffleNet achieve more accuracy than state-of-the-art approaches. Additionally, the proposed method is superior lightweight and suitable with a small system like a mobile healthcare system.

Adjunctive Diagnostic Methods for Skin Cancer Detection: A Review of Electrical Impedance-Based Techniques

Skin cancer is among the fastest-growing cancers with an excellent prognosis, if detected early. However, the current method of diagnosis by visual inspection has several disadvantages such as overlapping tumor characteristics, subjectivity, low sensitivity, and specificity. Hence, several adjunctive diagnostic techniques such as thermal imaging, optical imaging, ultrasonography, tape stripping methods, and electrical impedance imaging are employed along with visual inspection to improve the diagnosis. Electrical impedance-based skin cancer detection depends upon the variations in electrical impedance characteristics of the transformed cells. The information provided by this technique is fundamentally different from other adjunctive techniques and thus has good prospects. Depending on the stage, type, and location of skin cancer, various impedance-based devices have been developed. These devices when used as an adjunct to visual methods have increased the sensitivity and specificity of skin cancer detection up to 100% and 87%, respectively, thus demonstrating their potential to minimize unnecessary biopsies. In this review, the authors track the advancements and progress made in this technique for the detection of skin cancer, focusing mainly on the advantages and limitations in the clinical setting. © 2022 Bioelectromagnetics Society.

Skin chromophore mapping by smartphone RGB camera under spectral band and spectral line illumination

SIGNIFICANCE

Multispectral imaging enables mapping of chromophore content changes in skin neoplasms, which helps to diagnose a pathology. Different types of light sources can be used for the imaging. Design of laser-based illuminators is more complicated and, consequently, they are more expensive than LED-based illuminators. On the other hand, spectral line illumination has the advantage of less complicated calculations, since only the discrete maximum wavelengths need to be considered. Spectral band and spectral line approaches for multispectral skin diagnostics have not been compared so far. This can help to evaluate the accuracy and effectiveness of both approaches.

AIM

To compare two specific illumination modalities-spectral band and spectral line illumination-from the point of performance for mapping of in vivo skin chromophores.

APPROACH

Three spectral images of the same skin malformations were captured by a smartphone RGB camera with two different add-on illuminators comprising LED emitters and laser emitters, respectively. Five types of benign skin neoplasms were included in our study. Concentrations of skin melanin, oxy- and deoxy-hemoglobin at image pixel groups were calculated using the Beer-Lambert law.

RESULTS

Skin chromophore maps and statistical analysis of mean concentrations' changes in the neoplasms compared to the surrounding skin are presented and discussed. The data of the laser emitters led to significantly higher (∼10 times) increase of the oxy-hemoglobin values in vascular neoplasms and much lower deoxy-hemoglobin values, if compared to the data obtained by the LED emitters.

CONCLUSIONS

Analysis of the obtained chromophore distribution maps and concentration variations in neoplasms led to conclusion that the spectral line illumination approach is more appropriate for this application. Considering only the peak wavelengths of illumination spectral bands leads to essentially different results if compared to those obtained by spectral line illumination and may cause misinterpretations in the clinical assessment of skin neoplasms.

Photodynamic Therapy—An Up-to-Date Review

The healing power of light has attracted interest for thousands of years. Scientific discoveries and technological advancements in the field have eventually led to the emergence of photodynamic therapy, which soon became a promising approach in treating a broad range of diseases. Based on the interaction between light, molecular oxygen, and various photosensitizers, photodynamic therapy represents a non-invasive, non-toxic, repeatable procedure for tumor treatment, wound healing, and pathogens inactivation. However, classic photosensitizing compounds impose limitations on their clinical applications. Aiming to overcome these drawbacks, nanotechnology came as a solution for improving targeting efficiency, release control, and solubility of traditional photosensitizers. This paper proposes a comprehensive path, starting with the photodynamic therapy mechanism, evolution over the years, integration of nanotechnology, and ending with a detailed review of the most important applications of this therapeutic approach.