Basal cell carcinoma images
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Summary
10 papers analyzed
These studies suggest that various imaging techniques, including deep learning algorithms, reflectance confocal microscopy, multiphoton microscopy, terahertz pulse imaging, and optical coherence tomography, show promise in effectively detecting and diagnosing basal cell carcinoma.
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Advances in Imaging Techniques for Basal Cell Carcinoma Detection
Deep Learning Algorithms for Basal Cell Carcinoma Classification
Recent advancements in deep learning have significantly improved the classification of basal cell carcinoma (BCC) from clinical images. A study utilized a convolutional neural network (Microsoft ResNet-152) to classify images of various skin diseases, including BCC. The model was trained on a large dataset and validated on multiple datasets, achieving high diagnostic accuracy with an area under the curve (AUC) of 0.96 for BCC on the Asan dataset and 0.90 on the Edinburgh dataset. This performance was comparable to that of experienced dermatologists, highlighting the potential of deep learning in clinical settings.
Reflectance Confocal Microscopy (RCM) for Non-Invasive Diagnosis
Reflectance confocal microscopy (RCM) has emerged as a promising non-invasive imaging modality for diagnosing BCC. A systematic review of RCM's diagnostic accuracy reported a sensitivity of 0.97 and specificity of 0.93, indicating its high reliability in detecting BCC. Additionally, deep learning models applied to RCM images have shown significant potential, achieving an AUC of 89.7% at the stack level and 88.3% at the lesion level, which is on par with expert evaluations. These findings suggest that RCM, combined with AI, can reduce unnecessary biopsies and improve diagnostic accuracy.
Multiphoton Microscopy (MPM) for In Vivo Imaging
Multiphoton microscopy (MPM) offers another non-invasive technique for imaging BCC lesions in vivo. MPM can identify key histopathologic features of BCC, such as nests of basaloid cells, which correlate well with traditional histopathologic examinations. This technique provides label-free contrast and can potentially expedite the diagnosis and treatment process by reducing the need for invasive procedures.
Terahertz Pulse Imaging for Ex Vivo Analysis
Terahertz pulse imaging has been explored for studying BCC ex vivo. This technique uses the terahertz frequency range to differentiate between normal and diseased tissue based on absorption differences. In a study involving 21 samples, terahertz imaging successfully identified BCC in 17 cases, with results confirmed by histology. This method shows promise for surgical planning by providing high-resolution images that delineate tumor boundaries.
Smartphone-Captured Digital Histopathology
Innovations in digital histopathology have extended to smartphone-captured images. A deep neural network was developed to recognize BCC from these images, demonstrating the feasibility of using portable devices for accurate cancer detection. This approach could make BCC diagnosis more accessible, especially in resource-limited settings.
Line-Field Confocal Optical Coherence Tomography (LC-OCT)
Line-field confocal optical coherence tomography (LC-OCT) has been shown to increase diagnostic accuracy and confidence in identifying BCC, particularly in clinically equivocal cases. LC-OCT provides high-resolution images that correlate well with histopathology, improving sensitivity and specificity when combined with dermoscopy. This technique enhances the ability to distinguish between BCC subtypes and other skin conditions, facilitating more precise treatment planning.
Polarization-Sensitive Optical Coherence Tomography (PS-OCT)
Polarization-sensitive optical coherence tomography (PS-OCT) has been employed for the automated detection of BCC. By extracting quantitative features from intensity and phase retardation data, a machine-learning classifier achieved a sensitivity and specificity of 95.4%. This method underscores the importance of advanced imaging contrasts in improving diagnostic accuracy.
Conclusion
The integration of advanced imaging techniques and artificial intelligence has revolutionized the detection and classification of basal cell carcinoma. From deep learning algorithms and RCM to MPM and terahertz imaging, these innovations offer non-invasive, accurate, and efficient diagnostic solutions. As these technologies continue to evolve, they hold the promise of enhancing early detection, reducing unnecessary biopsies, and improving patient outcomes in the management of BCC.
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