Live, remote control of an in vivo reflectance confocal microscope for diagnosis of basal cell carcinoma at the bedside of a patient 2500 miles away: A novel tele-reflectance confocal microscope approach
“…Similarly, confocal microscopy is a means of providing noninvasive histomorphological analysis of skin lesions [42]. In a recent case in Los Angeles, reflectance confocal microscopy was used during a live-interactive teledermatology session to diagnose a nodular basal cell carcinoma [43]. The diagnosis took around 15 min, and the patient was pleased to understand their condition and discuss options for treatment immediately [43].…”
Remote consultations likely will grow in importance if the COVID-19 pandemic continues. This review analyzes which methods of teledermatology patients prefer by categorizing how recent studies have defined satisfaction, conducted surveys and concluded patients respond to the different modalities of teledermatology. Using PubMed and Cochrane databases, we reviewed studies from April 5th, 2010 to April 5th, 2020 that included the search terms patient satisfaction and teledermatology. All studies that included patient satisfaction as an outcome were included, but studies not published in English were excluded. We examined domains of satisfaction, survey method, study characteristics (including patient population, country, age, study design and evidence score), findings and statistical comparisons. We thoroughly reviewed 23 studies. Definitions of satisfaction varied, but all concluded patients were satisfied with the live-interactive and store-and-forward modalities. The studies reveal that store-and-forward is appropriate for clinicians with established patients who require regular followup. Verified areas of care include treatment of chronic conditions, topical skin cancer therapy, wound monitoring, and postprocedural follow-up. Only four studies conducted statistical analyses. One of those studies compared patient preference for each modality of teledermatology with face-to-face dermatology. While this study reported high satisfaction with each mode of teledermatology, patients still preferred face-to-face. Favorable responses to remote diagnostic capabilities suggest that these offerings improve preference for teledermatology. With only one study evaluating preference between each modality and face-to-face dermatology, more studies should address the discrepancy. Surveys that cover all domains of satisfaction may improve assessments and identify where gaps in preference exist.
“…Similarly, confocal microscopy is a means of providing noninvasive histomorphological analysis of skin lesions [42]. In a recent case in Los Angeles, reflectance confocal microscopy was used during a live-interactive teledermatology session to diagnose a nodular basal cell carcinoma [43]. The diagnosis took around 15 min, and the patient was pleased to understand their condition and discuss options for treatment immediately [43].…”
Remote consultations likely will grow in importance if the COVID-19 pandemic continues. This review analyzes which methods of teledermatology patients prefer by categorizing how recent studies have defined satisfaction, conducted surveys and concluded patients respond to the different modalities of teledermatology. Using PubMed and Cochrane databases, we reviewed studies from April 5th, 2010 to April 5th, 2020 that included the search terms patient satisfaction and teledermatology. All studies that included patient satisfaction as an outcome were included, but studies not published in English were excluded. We examined domains of satisfaction, survey method, study characteristics (including patient population, country, age, study design and evidence score), findings and statistical comparisons. We thoroughly reviewed 23 studies. Definitions of satisfaction varied, but all concluded patients were satisfied with the live-interactive and store-and-forward modalities. The studies reveal that store-and-forward is appropriate for clinicians with established patients who require regular followup. Verified areas of care include treatment of chronic conditions, topical skin cancer therapy, wound monitoring, and postprocedural follow-up. Only four studies conducted statistical analyses. One of those studies compared patient preference for each modality of teledermatology with face-to-face dermatology. While this study reported high satisfaction with each mode of teledermatology, patients still preferred face-to-face. Favorable responses to remote diagnostic capabilities suggest that these offerings improve preference for teledermatology. With only one study evaluating preference between each modality and face-to-face dermatology, more studies should address the discrepancy. Surveys that cover all domains of satisfaction may improve assessments and identify where gaps in preference exist.
“…If indicated, all or part of the specimen can be preserved for molecular analysis as the tissue is neither processed nor sectioned. Finally, since the FF-OCT images are digitally stored, they can be read and analyzed remotely by a specialist, as a telehealth tool [32], for evaluation of ex vivo tissue, especially beneficial for rural or underserved areas. Although different ex vivo imaging technologies exist, knowledge of this novel device is essential to the consumers so they can tailor their needs based on the device's cost and capability.…”
Histopathology for tumor margin assessment is time‐consuming and expensive. High‐resolution full‐field optical coherence tomography (FF‐OCT) images fresh tissues rapidly at cellular resolution and potentially facilitates evaluation. Here, we define FF‐OCT features of normal and neoplastic skin lesions in fresh ex vivo tissues and assess its diagnostic accuracy for malignancies. For this, normal and neoplastic tissues were obtained from Mohs surgery, imaged using FF‐OCT, and their features were described. Two expert OCT readers conducted a blinded analysis to evaluate their diagnostic accuracies, using histopathology as the ground truth. A convolutional neural network was built to distinguish and outline normal structures and tumors. Of the 113 tissues imaged, 95 (84%) had a tumor (75 basal cell carcinomas [BCCs] and 17 squamous cell carcinomas [SCCs]). The average reader diagnostic accuracy was 88.1%, with a sensitivity of 93.7%, and a specificity of 58.3%. The artificial intelligence (AI) model achieved a diagnostic accuracy of 87.6 ± 5.9%, sensitivity of 93.2 ± 2.1%, and specificity of 81.2 ± 9.2%. A mean intersection‐over‐union of 60.3 ± 10.1% was achieved when delineating the nodular BCC from normal structures. Limitation of the study was the small sample size for all tumors, especially SCCs. However, based on our preliminary results, we envision FF‐OCT to rapidly image fresh tissues, facilitating surgical margin assessment. AI algorithms can aid in automated tumor detection, enabling widespread adoption of this technique.
“…If indicated, all or part of the specimen can be preserved for molecular analysis as the tissue is neither processed nor sectioned. Lastly, since the FF-OCT images are digitally stored they can be read and analyzed remotely by a specialist, as a telehealth tool 29 , for evaluation of ex vivo tissue, especially bene cial for rural or underserved areas. Although different ex vivo imaging technologies exists, knowledge of this novel device is essential to the consumers so they can tailor their needs based on the device cost and capability.…”
Histopathology for tumor margin assessment is time-consuming and expensive. High-resolution full-field optical coherence tomography (FF-OCT) images fresh tissues rapidly at cellular resolution and potentially facilitates evaluation. Here, we define FF-OCT features of normal and neoplastic skin lesions in fresh ex vivo tissues and assess its diagnostic accuracy for malignancies. For this, normal and neoplastic tissues were obtained from Mohs surgery, imaged using FF-OCT, and their features were described. Two expert OCT readers conducted a blinded analysis to evaluate their diagnostic accuracies, using histopathology as the ground truth. A convolutional neural network was built to distinguish and outline normal structures and tumors. Of the 113 tissues imaged, 95 (84%) had a tumor (75 BCCs and 17 SCCs). The average reader diagnostic accuracy was 88.1%, with, a sensitivity of 93.7%, and a specificity of 58.3%. The AI model achieved a diagnostic accuracy of 87.6%±5.9%, sensitivity of 93.2%±2.1%, and specificity of 81.2%±9.2%. A mean intersection-over-union of 60.3%±10.1% was achieved when delineating the nodular BCC from normal structures. Limitation of the study was the small sample size for all tumors, especially SCCs. However, based on our preliminary results, we envision FF-OCT to rapidly image fresh tissues, facilitating surgical margin assessment. AI algorithms can aid in automated tumor detection, enabling widespread adoption of this technique.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.