Smartphone fluorescence imager for quantitative dosimetry of protoporphyrin-IX-based photodynamic therapy in skin

Artificial intelligence-assisted grading for tear trough deformity

BACKGROUND

Various classification systems for tear trough deformity (TTD) have been published; however, their complexity can pose challenges in clinical use, especially for less experienced surgeons. It is believed that artificial intelligence (AI) technology can address some of these challenges by reducing inadvertent errors and improving the accuracy of medical practice. In this study, we aimed to establish a reliable and precise digital image grading model for TTD using smartphone-based photography enhanced using AI deep learning technology. This model is designed to aid and guide surgeons, particularly those who are less experienced or from younger generations, during clinical examinations and in making decisions regarding further surgical interventions.

MATERIALS AND METHODS

A total of 504 patients and 983 photos were included in the study. We adopted the Barton's grading system for TTD. All photos were taken using the same smartphone and processed and analyzed using the medical AI assistant (MAIA™) software. The photos were then randomly divided into two groups to establish training and testing models.

RESULTS

The confusion matrix for the training model demonstrated a sensitivity of 56%, specificity of 87.3%, F1 score of 0.57, and an area under the curve (AUROC) of 0.85. For the testing group, the sensitivity was 49.3%, specificity was 85%, F1 score was 0.49, and AUROC was 0.83. Representative heatmaps were also generated.

CONCLUSION

Our study is the first to demonstrate that tear trough deformities can be easily categorized using a built-in smartphone camera in conjunction with an AI deep learning program. This approach can reduce errors during clinical patient evaluations, particularly for less experienced practitioners.

Devices and Methods for Dosimetry of Personalized Photodynamic Therapy of Tumors: A Review on Recent Trends

Significance: Despite the widespread use of photodynamic therapy in clinical practice, there is a lack of personalized methods for assessing the sufficiency of photodynamic exposure on tumors, depending on tissue parameters that change during light irradiation. This can lead to different treatment results. Aim: The objective of this article was to conduct a comprehensive review of devices and methods employed for the implicit dosimetric monitoring of personalized photodynamic therapy for tumors. Methods: The review included 88 peer-reviewed research articles published between January 2010 and April 2024 that employed implicit monitoring methods, such as fluorescence imaging and diffuse reflectance spectroscopy. Additionally, it encompassed computer modeling methods that are most often and successfully used in preclinical and clinical practice to predict treatment outcomes. The Internet search engine Google Scholar and the Scopus database were used to search the literature for relevant articles. Results: The review analyzed and compared the results of 88 peer-reviewed research articles presenting various methods of implicit dosimetry during photodynamic therapy. The most prominent wavelengths for PDT are in the visible and near-infrared spectral range such as 405, 630, 660, and 690 nm. Conclusions: The problem of developing an accurate, reliable, and easily implemented dosimetry method for photodynamic therapy remains a current problem, since determining the effective light dose for a specific tumor is a decisive factor in achieving a positive treatment outcome.

Smartphone-based dual radiometric fluorescence and white-light imager for quantification of protoporphyrin IX in skin

Significance

The quantification of protoporphyrin IX (PpIX) in skin can be used to study photodynamic therapy (PDT) treatments, understand porphyrin mechanisms, and enhance preoperative mapping of non-melanoma skin cancers.

Aim

We aim to develop a smartphone-based imager for performing simultaneous radiometric fluorescence (FL) and white light (WL) imaging to study the baseline levels, accumulation, and photobleaching of PpIX in skin.

Approach

A smartphone-based dual FL and WL imager (sDUO) is introduced alongside new radiometric calibration methods for providing SI-units of measurements in both pre-clinical and clinical settings. These radiometric measurements and corresponding PpIX concentration estimations are applied to clinical measurements to understand mechanistic differences between PDT treatments, accumulation differences between normal tissue and actinic keratosis lesions, and the correlation of photosensitizer concentrations to treatment outcomes.

Results

The sDUO alongside the developed methods provided radiometric FL measurements (nW / cm 2 ) with a demonstrated sub nanomolar PpIX sensitivity in 1% intralipid phantoms. Patients undergoing PDT treatment of actinic keratosis (AK) lesions were imaged, capturing the increase and subsequent decrease in FL associated with the incubation and irradiation timepoints of lamp-based PDT. Furthermore, the clinical measurements showed mechanistic differences in new daylight-based treatment modalities alongside the selective accumulation of PpIX within AK lesions. The use of the radiometric calibration enabled the reporting of detected PpIX FL in units of nW / cm 2 with the use of liquid phantom measurements allowing for the estimation of in-vivo molar concentrations of skin PpIX.

Conclusions

The phantom, pre-clinical, and clinical measurements demonstrated the capability of the sDUO to provide quantitative measurements of PpIX FL. The results demonstrate the use of the sDUO for the quantification of PpIX accumulation and photobleaching in a clinical setting, with implications for improving the diagnosis and treatment of various skin conditions.

Adaptive Design of Fluorescence Imaging Systems for Custom Resolution, Fields of View, and Geometries

Objective and Impact Statement: We developed a generalized computational approach to design uniform, high-intensity excitation light for low-cost, quantitative fluorescence imaging of in vitro, ex vivo, and in vivo samples with a single device. Introduction: Fluorescence imaging is a ubiquitous tool for biomedical applications. Researchers extensively modify existing systems for tissue imaging, increasing the time and effort needed for translational research and thick tissue imaging. These modifications are application-specific, requiring new designs to scale across sample types. Methods: We implemented a computational model to simulate light propagation from multiple sources. Using a global optimization algorithm and a custom cost function, we determined the spatial positioning of optical fibers to generate 2 illumination profiles. These results were implemented to image core needle biopsies, preclinical mammary tumors, or tumor-derived organoids. Samples were stained with molecular probes and imaged with uniform and nonuniform illumination. Results: Simulation results were faithfully translated to benchtop systems. We demonstrated that uniform illumination increased the reliability of intraimage analysis compared to nonuniform illumination and was concordant with traditional histological findings. The computational approach was used to optimize the illumination geometry for the purposes of imaging 3 different fluorophores through a mammary window chamber model. Illumination specifically designed for intravital tumor imaging generated higher image contrast compared to the case in which illumination originally optimized for biopsy images was used. Conclusion: We demonstrate the significance of using a computationally designed illumination for in vitro, ex vivo, and in vivo fluorescence imaging. Application-specific illumination increased the reliability of intraimage analysis and enhanced the local contrast of biological features. This approach is generalizable across light sources, biological applications, and detectors.

Ultracompact fluorescence smartphone attachment using built-in optics for protoporphyrin-IX quantification in skin

Smartphone-based fluorescence imaging systems have the potential to provide convenient quantitative image guidance at the point of care. However, common approaches have required the addition of complex optical attachments, which reduce translation potential. In this study, a simple clip-on attachment appropriate for fluorescence imaging of protoporphyrin-IX (PpIX) in skin was designed using the built-in light source and ultrawide camera sensor of a smartphone. Software control for image acquisition and quantitative analysis was developed using the 10-bit video capability of the phone. Optical performance was characterized using PpIX in liquid tissue phantoms and endogenously produced PpIX in mice and human skin. The proposed system achieves a very compact form factor (<30 cm3) and can be readily fabricated using widely available low-cost materials. The limit of detection of PpIX in optical phantoms was <10 nM, with good signal linearity from 10 to 1000 nM (R2 >0.99). Both murine and human skin imaging verified that in vivo PpIX fluorescence was detected within 1 hour of applying aminolevulinic acid (ALA) gel. This ultracompact handheld system for quantification of PpIX in skin is well-suited for dermatology clinical workflows. Due to its simplicity and form factor, the proposed system can be readily adapted for use with other smartphone devices and fluorescence imaging applications. Hardware design and software for the system is made freely available on GitHub (https://github.com/optmed/CompactFluorescenceCam).

Use of digital photography to identify neoplastic skin lesions after labelling by ALA-derived protoporphyrin

Actinic keratosis is a premalignant skin lesion that develops into non-melanoma skin cancer. Various imaging techniques have been developed to find the actinic keratosis lesion. In this clinical study, the feasibility of a nonspectroscopic fluorescence imaging system is investigated for spatial assessment of the actinic keratosis lesion. Six patients between the ages of 70 and 80 years old are diagnosed with actinic keratosis by a board-certified dermatologist to obtain biopsy-proven clinical images. The patients were treated with 5-aminolevulinic acid, which is transformed into the protoporphyrin IX. After illuminating ultraviolet-A light on facial lesions, the protoporphyrin IX produces the exogenous fluorescence. The fluorescence is measured using both a hyperspectral camera and an RGB color camera to obtain spectroscopic and nonspectroscopic fluorescence images, respectively. It is found that fluorescence intensity of the actinic keratosis lesion is higher than that of normal skin. Based on combined fluorescence and physiological characteristics, the actinic keratosis lesion is distinguished from the adjacent normal skin area. For delineation of the actinic keratosis lesion, a linear unmixing algorithm is applied to spectroscopic image data and an erythema index is calculated from nonspectroscopic image data. Then, two extracted actinic keratosis lesions are compared for cross-validation. As a result, both spectroscopic and nonspectroscopic fluorescence images demarcate an identical lesion of actinic keratosis. Given the affordability and simplicity, an RGB camera and a 5-ALA photosensitizer can be used as a cost-effective nonspectroscopic imaging modality for accurate assessment of actinic keratosis margins.

[Artificial intelligence and smartphone program applications (Apps) : Relevance for dermatological practice]

ADVANTAGES OF ARTIFICIAL INTELLIGENCE (AI)

With responsible, safe and successful use of artificial intelligence (AI), possible advantages in the field of dermato-oncology include the following: (1) medical work can focus on skin cancer patients, (2) patients can be more quickly and effectively treated despite the increasing incidence of skin cancer and the decreasing number of actively working dermatologists and (3) users can learn from the AI results. POTENTIAL DISADVANTAGES AND RISKS OF AI USE: (1) Lack of mutual trust can develop due to the decreased patient-physician contact, (2) additional time effort will be necessary to promptly evaluate the AI-classified benign lesions, (3) lack of adequate medical experience to recognize misclassified AI decisions and (4) recontacting a patient in due time in the case of incorrect AI classifications. Still problematic in the use of AI are the medicolegal situation and remuneration. Apps using AI currently cannot provide sufficient assistance based on clinical images of skin cancer.

REQUIREMENTS AND POSSIBLE USE OF SMARTPHONE PROGRAM APPLICATIONS

Smartphone program applications (apps) can be implemented responsibly when the image quality is good, the patient's history can be entered easily, transmission of the image and results are assured and medicolegal aspects as well as remuneration are clarified. Apps can be used for disease-specific information material and can optimize patient care by using teledermatology.