In vivo imaging of cervical precancer using a low-cost and easy-to-use confocal microendoscope

Tang, Yubo; Kortum, Alex; Parra, Sonia; Vohra, Imran S.; Milbourne, Andrea; Ramalingam, Preetha; Toscano, Paul A.; Schmeler, Kathleen M.; Richards‐Kortum, Rebecca

doi:10.1364/boe.381064

Cited by 13 publications

(8 citation statements)

References 27 publications

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“…As machine learning is a very dynamic field of research, future developments could supersede this multi‐task CNN and further improve automated image analysis with HRME. In addition to improved image analysis, a low‐cost confocal HRME has recently been demonstrated to improve the image contrast of nuclear morphometry in columnar cervical tissue 27 . Coupling multi‐task CNN analysis with confocal imaging has potential for further diagnostic accuracy of HRME with automated image analysis.…”

Section: Discussionmentioning

confidence: 99%

Cervical lesion assessment using real‐time microendoscopy image analysis in Brazil: The CLARA study

Hunt

Fregnani

Brenes

et al. 2021

Intl Journal of Cancer

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We conducted a prospective evaluation of the diagnostic performance of high-resolution microendoscopy (HRME) to detect cervical intraepithelial neoplasia (CIN) in women with abnormal screening tests. Study participants underwent colposcopy, HRME and cervical biopsy. The prospective diagnostic performance of HRME using an automated morphologic image analysis algorithm was compared to that of colposcopy using histopathologic detection of CIN as the gold standard. To assess the potential to further improve performance of HRME image analysis, we also conducted a retrospective analysis assessing performance of a multi-task convolutional neural network to segment and classify HRME images. One thousand four hundred eighty-six subjects completed the study; 435 (29%) subjects had CIN Grade 2 or more severe (CIN2+) diagnosis. HRME with morphologic image analysis for detection of CIN Grade 3 or more severe diagnoses (CIN3+) was similarly sensitive (95.6% vs 96.2%, P = .81) and specific (56.6% vs 58.7%, P = .18) as colposcopy. HRME with morphologic image analysis for detection of CIN2+ was slightly less sensitive (91.7% vs 95.6%, P < .01) and specific (59.7% vs 63.4%, P = .02) than colposcopy. Images from 870 subjects were used to train a multi-task convolutional neural network-based algorithm and images from the remaining 616 were used to validate its performance. There were no significant differences in the sensitivity and specificity of HRME with neural network analysis vs colposcopy for detection of CIN2+ or CIN3+.Using a neural network-based algorithm, HRME has comparable sensitivity and specificity to colposcopy for detection of CIN2+. HRME could provide a low-cost, point-of-care alternative to colposcopy and biopsy in the prevention of cervical cancer.

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Section: Discussionmentioning

confidence: 99%

Cervical lesion assessment using real‐time microendoscopy image analysis in Brazil: The CLARA study

Hunt

Fregnani

Brenes

et al. 2021

Intl Journal of Cancer

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“…Second, DeepDOF images were acquired without optical sectioning. While non-optical-sectioned images of proflavine-stained nuclear morphology have been found to show great diagnostic value in a wide range of clinical applications ( 51 – 54 ), image quality can be further improved with confocal scanning or structured illumination ( 55 , 56 ). This can be particularly important when exploring the use of other nonspecific staining agents, endogenous fluorophores, or label-free modalities.…”

Section: Discussionmentioning

confidence: 99%

Deep learning extended depth-of-field microscope for fast and slide-free histology

Jin

Tang

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained, and imaged to determine whether surgical margins are free of tumor cells—a costly and time- and labor-intensive procedure. Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning. The DeepDOF microscope consists of a conventional fluorescence microscope with the simple addition of an inexpensive (less than $10) phase mask inserted in the pupil plane to encode the light field and enhance the depth-invariance of the point-spread function. When used with a jointly optimized image-reconstruction algorithm, diffraction-limited optical performance to resolve subcellular features can be maintained while significantly extending the DOF (200 µm). Data from resected oral surgical specimens show that the DeepDOF microscope can consistently visualize nuclear morphology and other important diagnostic features across highly irregular resected tissue surfaces without serial refocusing. With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue sampling during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings.

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“…To further facilitate dissemination of this high‐resolution yet inexpensive technology, quantitative and automated diagnostic algorithms have been developed and prospectively validated [ 97 , 99 , 100 ]. Recent development focused on further improvement of its axial resolution with optical sectioning techniques [ 101 , 102 ]. Compared with commercial CLE, HRME offers a low‐cost alternative to visualize important histologic features in community and resource‐constrained settings.…”

Section: Microscopic Imaging Systemsmentioning

confidence: 99%

Advances in optical gastrointestinal endoscopy: a technical review

2020

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Optical endoscopy is the primary diagnostic and therapeutic tool for management of gastrointestinal (GI) malignancies. Most GI neoplasms arise from precancerous lesions; thus, technical innovations to improve detection and diagnosis of precancerous lesions and early cancers play a pivotal role in improving outcomes. Over the last few decades, the field of GI endoscopy has witnessed enormous and focused efforts to develop and translate accurate, user‐friendly, and minimally invasive optical imaging modalities. From a technical point of view, a wide range of novel optical techniques is now available to probe different aspects of light–tissue interaction at macroscopic and microscopic scales, complementing white light endoscopy. Most of these new modalities have been successfully validated and translated to routine clinical practice. Herein, we provide a technical review of the current status of existing and promising new optical endoscopic imaging technologies for GI cancer screening and surveillance. We summarize the underlying principles of light–tissue interaction, the imaging performance at different scales, and highlight what is known about clinical applicability and effectiveness. Furthermore, we discuss recent discovery and translation of novel molecular probes that have shown promise to augment endoscopists' ability to diagnose GI lesions with high specificity. We also review and discuss the role and potential clinical integration of artificial intelligence‐based algorithms to provide decision support in real time. Finally, we provide perspectives on future technology development and its potential to transform endoscopic GI cancer detection and diagnosis.

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In vivo imaging of cervical precancer using a low-cost and easy-to-use confocal microendoscope

Cited by 13 publications

References 27 publications

Cervical lesion assessment using real‐time microendoscopy image analysis in Brazil: The CLARA study

Cervical lesion assessment using real‐time microendoscopy image analysis in Brazil: The CLARA study

Deep learning extended depth-of-field microscope for fast and slide-free histology

Advances in optical gastrointestinal endoscopy: a technical review

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