Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer

Artificial intelligence for nonmelanoma skin cancer

Nonmelanoma skin cancers (NMSCs) are among the top five most common cancers globally. NMSC is an area with great potential for novel application of diagnostic tools including artificial intelligence (AI). In this scoping review, we aimed to describe the applications of AI in the diagnosis and treatment of NMSC. Twenty-nine publications described AI applications to dermatopathology including lesion classification and margin assessment. Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training data sets. Sixteen publications described the use of AI in cutaneous surgery for NMSC including use in margin assessment during excisions and Mohs surgery, as well as predicting procedural complexity. Eleven publications discussed spectroscopy, confocal microscopy, thermography, and the AI algorithms that analyze and interpret their data. Ten publications pertained to AI applications for the discovery and use of NMSC biomarkers. Eight publications discussed the use of smartphones and AI, specifically how they enable clinicians and patients to have increased access to instant dermatologic assessments but with varying accuracies. Five publications discussed large language models and NMSC, including how they may facilitate or hinder patient education and medical decision-making. Three publications pertaining to the skin of color and AI for NMSC discussed concerns regarding limited diverse data sets for the training of convolutional neural networks. AI demonstrates tremendous potential to improve diagnosis, patient and clinician education, and management of NMSC. Despite excitement regarding AI, data sets are often not transparently reported, may include low-quality images, and may not include diverse skin types, limiting generalizability. AI may serve as a tool to increase access to dermatology services for patients in rural areas and save health care dollars. These benefits can only be achieved, however, with consideration of potential ethical costs.

Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions

Radiological image analysis using machine learning has been extensively applied to enhance biopsy diagnosis accuracy and assist radiologists with precise cures. With improvements in the medical industry and its technology, computer-aided diagnosis (CAD) systems have been essential in detecting early cancer signs in patients that could not be observed physically, exclusive of introducing errors. CAD is a detection system that combines artificially intelligent techniques with image processing applications thru computer vision. Several manual procedures are reported in state of the art for cancer diagnosis. Still, they are costly, time-consuming and diagnose cancer in late stages such as CT scans, radiography, and MRI scan. In this research, numerous state-of-the-art approaches on multi-organs detection using clinical practices are evaluated, such as cancer, neurological, psychiatric, cardiovascular and abdominal imaging. Additionally, numerous sound approaches are clustered together and their results are assessed and compared on benchmark datasets. Standard metrics such as accuracy, sensitivity, specificity and false-positive rate are employed to check the validity of the current models reported in the literature. Finally, existing issues are highlighted and possible directions for future work are also suggested.

Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine

Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.

[Prospects for the use of autofluorescence endoscopy in rhinosurgery]

The condition of blood supply and microcirculation in the mucous membrane is considered to be the main factor determining the consistency of local flaps of the nasal mucous membrane.

OBJECTIVE

To assess the possibility of using autofluorescence endoscopy to control the condition of flaps of the nasal mucous membrane after surgical interventions.

MATERIAL AND METHODS

The assessment of the processes of biological destruction of 15 fragments of the mucous membrane of the inferior nasal concha, obtained during intranasal interventions with endoscopic technique in autofluorescence mode, followed by histological examination at different periods of observation, has been conducted. The results of the proposed method were tested in 84 patients with chronic and intraoperative defects of the nasal septum after the surgical intervention.

RESULTS

It has been shown that autofluorescence endoscopy can be used to assess the viability of flaps of the nasal cavity's mucous membrane. A change of the autofluorescence signal to a whitish and orange shades indicates the presence of ischemia and necrosis. Method sensitivity was 92.3%, specificity was 95.8% and total accuracy was 95.4%.

CONCLUSION

This method is promising for predicting the long-term outcomes of surgical treatment of the nasal septum's perforation in relation to the risk of reperforation forming, as well as for assessment of the functional viability of local flaps in plastic reconstruction of defects of the anterior skull base.

Preliminary low-dose photodynamic exposure to the skin cancer with chlorin e6 photosensitizer

BACKGROUND

The study was aimed to investigate the chlorin e6 photosensitizer distribution in the tumor and tumor border (5 mm) during low-dose photodynamic treatment and to increase the effectiveness of the therapy for skin neoplasms.

METHODS

Sensitized boundaries of neoplasms were evaluated by video fluorescence imaging. The study of changes in the chlorin e6 distribution before/after photodynamic therapy and in the process of low-dose photodynamic exposure was carried out by the method of spectral fluorescence diagnostics.

RESULTS

All 19 patients with basal-cell skin cancer had a contrast of chlorin e6 accumulation compared to normal tissues. 3 hours after intravenous administration of the photosensitizer at a dose of 1 mg/kg, the chlorin e6 concentration was: in normal tissues - 0.18 mg/kg, in the tumor - 1.26 mg/kg, in the tumor border - 0.63 mg/kg. In most cases, the fluorescence indices of chlorin e6 in tumor tissues after low-dose photodynamic therapy increased and exceeded the values before light exposure.

CONCLUSION

Low-dose photodynamic therapy seems to be the optimal method for treating neoplasms, which does not cause severe pain in patients during the light exposure and allows locally increasing of the photosensitizer concentration in tumor tissues. This method of photodynamic therapy can improve the effectiveness of thе treatment.

Applications of Laser-Induced Fluorescence in Medicine

Fluorescence is the most sensitive spectroscopic method of analysis and fluorescence methods. However, classical analysis requires sampling. There are new needs for real-time analyses of biological materials, without the need for sampling. This article presents examples of proprietary applications of laser-induced fluorescence (LIF) in medicine with such methods. A classic example is the analysis of photosensitizers using the photodynamic treatment method (PDT). The level and kinetics of accumulation and excretion of sensitizers in the body are examined, as well as the optimal exposure time after the application of compounds. The LIF method is also used to analyze endogenous fluorophores; it has been used to detect neoplasms, e.g., lung cancer or gynecological and dermatological diseases. Furthermore, it is used for the diagnosis of early stages of tooth decay or detection of fungi. The article will present the construction of sensors based on the LIF method—fiber laser spectrometers and investigated fluorescence spectra in individual applications. Examples of fluorescence imaging, e.g., dermatological, and dental diagnostics and measuring systems will be presented. The advantage of the method is it has greater sensitivity and easily detects lesions early compared to the methods used in observing the material in reflected light.