Segmentation and Evaluation of Corneal Nerves and Dendritic Cells From In Vivo Confocal Microscopy Images Using Deep Learning

AxonFinder: Automated segmentation of tumor innervating neuronal fibers

Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the disease's advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach. Utilizing a dataset of whole-slide images from low-, intermediate-, and high-risk prostate cancer patients, we manually annotated axons to train our model, achieving significant accuracy in detecting axonal structures that were previously hard to segment. Our method achieves high performance, with a validation F1-score of 94 % and IoU of 90.78 %. Besides, the morphometric analysis that shows strong alignment between manual annotations and automated segmentation with nerve length and tortuosity closely matching manual measurements. Furthermore, our analysis includes a comprehensive assessment of axon density and morphological features across different CAPRA-S prostate cancer risk categories revealing a significant decline in axon density correlating with higher CAPRA-S prostate cancer risk scores. Our paper suggests the potential utility of neuronal markers in the prognostic assessment of prostate cancer in aiding the pathologist's assessment of tumor sections and advancing our understanding of neurosignaling in the tumor microenvironment.

Segmentation and multiparametric evaluation of corneal whorl-like nerves for in vivo confocal microscopy images in dry eye disease

OBJECTIVE

To establish an automated corneal nerve analysis system for corneal in vivo confocal microscopy (IVCM) images from both the whorl-like corneal nerves in the inferior whorl (IW) region and the straight ones in the central cornea and to characterise the geometric features of cornea nerves in dry eye disease (DED).

METHODS AND ANALYSIS

An encoder-decoder-based semi-supervised method was proposed for corneal nerve segmentation. This model's performance was compared with the ground truth provided by experienced clinicians, using Dice similarity coefficient (DSC), mean intersection over union (mIoU), accuracy (Acc), sensitivity (Sen) and specificity (Spe). The corneal nerve total length (CNFL), tortuosity (CNTor), fractal dimension (CNDf) and number of branching points (CNBP) were used for further analysis in an independent DED dataset including 50 patients with DED and 30 healthy controls.

RESULTS

The model achieved 95.72% Acc, 97.88% Spe, 80.61% Sen, 75.26% DSC, 77.57% mIoU and an area under the curve value of 0.98. For clinical evaluation, the CNFL, CNBP and CNDf for whorl-like and straight nerves showed a significant decrease in DED patients compared with healthy controls (p<0.05). Additionally, significantly elevated CNTor was detected in the IW in DED patients (p<0.05). The CNTor for straight corneal nerves, however, showed no significant alteration in DED patients (p>0.05).

CONCLUSION

The proposed method segments both whorl-like and straight corneal nerves in IVCM images with high accuracy and offered parameters to objectively quantify DED-induced corneal nerve injury. The IW is an effective region to detect alterations of multiple geometric indices in DED patients.

Three-Dimensional Reconstruction of Subbasal Nerve Density in Eyes With Limbal Stem Cell Deficiency: A Pilot Study

PURPOSE

Corneal subbasal nerve parameters have been previously reported using 2-dimensional scans of in vivo laser scanning confocal microscopy (IVCM) in eyes with limbal stem cell deficiency (LSCD). This study aims to develop and validate a method to better quantify corneal subbasal nerve parameters and changes from reconstructed 3-dimensional (3D) images.

METHODS

IVCM volume scans from 73 eyes with various degrees of LSCD (mild/moderate/severe) confirmed by multimodal anterior segment imaging including IVCM and 20 control subjects were included. Using ImageJ, the scans were manually aligned and compiled to generate a 3D reconstruction. Using filament-tracing semiautomated software (Imaris), subbasal nerve density (SND), corneal nerve fiber length, long nerves (>200 μm), and branch points were quantified and correlated with other biomarkers of LSCD.

RESULTS

3D SND decreased in eyes with LSCD when compared with control subjects. The decrease was significant for moderate and severe LSCD ( P < 0.01). 3D SND was reduced by 3.7% in mild LSCD, 32.4% in moderate LSCD, and 96.5% in severe LSCD. The number of long nerves and points of branching correlated with the severity of LSCD ( P < 0.0001) and with declining SND (R 2 = 0.66 and 0.67, respectively). When compared with 2-dimensional scans, 3D reconstructions yielded significant increases of SND and branch points in all conditions except severe LSCD. 3D analysis showed a 46% increase in long nerves only in mild LSCD ( P < 0.01).

CONCLUSIONS

This proof-of-concept study validates the use of 3D reconstruction to better characterize the corneal subbasal nerve in eyes with LSCD. In the future, this concept could be used with machine learning to automate the measurements.

Diabetic Peripheral Neuropathy: Emerging Treatments of Neuropathic Pain and Novel Diagnostic Methods

BACKGROUND

Diabetic peripheral neuropathy (DPN) is a prevalent and debilitating complication of diabetes, often leading to severe neuropathic pain. Although other diabetes-related complications have witnessed a surge of emerging treatments in recent years, DPN has seen minimal progression. This stagnation stems from various factors, including insensitive diagnostic methods and inadequate treatment options for neuropathic pain.

METHODS

In this comprehensive review, we highlight promising novel diagnostic techniques for assessing DPN, elucidating their development, strengths, and limitations, and assessing their potential as future reliable clinical biomarkers and endpoints. In addition, we delve into the most promising emerging pharmacological and mechanistic treatments for managing neuropathic pain, an area currently characterized by inadequate pain relief and a notable burden of side effects.

RESULTS

Skin biopsies, corneal confocal microscopy, transcutaneous electrical stimulation, blood-derived biomarkers, and multi-omics emerge as some of the most promising new techniques, while low-dose naltrexone, selective sodium-channel blockers, calcitonin gene-related peptide antibodies, and angiotensin type 2 receptor antagonists emerge as some of the most promising new drug candidates.

CONCLUSION

Our review concludes that although several promising diagnostic modalities and emerging treatments exist, an ongoing need persists for the further development of sensitive diagnostic tools and mechanism-based, personalized treatment approaches.

Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review

In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversible cause of blindness. This review explores AI's transformative role in the corneal subspecialty, which has adopted advanced technology for superior clinical judgment, early diagnosis, and personalized therapy. While AI's role in anterior segment diseases is less documented compared to glaucoma and retinal pathologies, this review highlights its integration into corneal diagnostics through imaging techniques like slit-lamp biomicroscopy, anterior segment optical coherence tomography (AS-OCT), and in vivo confocal biomicroscopy. AI has been pivotal in refining decision-making and prognosis for conditions such as keratoconus, infectious keratitis, and dystrophies. Multi-disease deep learning neural networks (MDDNs) have shown diagnostic ability in classifying corneal diseases using AS-OCT images, achieving notable metrics like an AUC of 0.910. AI's progress over two decades has significantly improved the accuracy of diagnosing conditions like keratoconus and microbial keratitis. For instance, AI has achieved a 90.7% accuracy rate in classifying bacterial and fungal keratitis and an AUC of 0.910 in differentiating various corneal diseases. Convolutional neural networks (CNNs) have enhanced the analysis of color-coded corneal maps, yielding up to 99.3% diagnostic accuracy for keratoconus. Deep learning algorithms have also shown robust performance in detecting fungal hyphae on in vivo confocal microscopy, with precise quantification of hyphal density. AI models combining tomography scans and visual acuity have demonstrated up to 97% accuracy in keratoconus staging according to the Amsler-Krumeich classification. However, the review acknowledges the limitations of current AI models, including their reliance on binary classification, which may not capture the complexity of real-world clinical presentations with multiple coexisting disorders. Challenges also include dependency on data quality, diverse imaging protocols, and integrating multimodal images for a generalized AI diagnosis. The need for interpretability in AI models is emphasized to foster trust and applicability in clinical settings. Looking ahead, AI has the potential to unravel the intricate mechanisms behind corneal pathologies, reduce healthcare's carbon footprint, and revolutionize diagnostic and management paradigms. Ethical and regulatory considerations will accompany AI's clinical adoption, marking an era where AI not only assists but augments ophthalmic care.

Multimodal Approach in Dry Eye Disease Combining In Vivo Confocal Microscopy and HLA-DR Expression

Purpose

The purpose of this study was to determine the association between corneal images provided by in vivo confocal microscopy (IVCM) with clinical parameters and conjunctival expression of HLA-DR antigen in patients with dry eye disease (DED).

Methods

Two hundred fourteen eyes of 214 patients with DED were analyzed, consisting of 2 groups of patients - 63 with autoimmune dry eye disease (AIDED) and 151 with non-autoimmune dry eye disease (NAIDED). Patients underwent a full clinical examination, including symptom screening, using the Ocular Surface Disease Index (OSDI) questionnaire, and objective analysis of DED signs by Schirmer's testing, tear break-up time (TBUT), Oxford's test, and IVCM corneal imaging. The IVCM scoring criteria were based on corneal sub-basal nerve density (ND), nerve morphology (NM), and inflammatory cell (IC) density. Quantification of conjunctival HLA-DR antigen was performed by flow cytometry.

Results

The total IVCM score (T-IVCM) as well as the IVCM-IC subscore (sc) were positively correlated with HLA-DR levels with r = 0.3, P < 0.001 and r = 0.3, P < 0.01, respectively in the total population of patients with DED. The IVCM-NDsc was negatively correlated with TBUT in patients with AIDED (r = -0.2, P < 0.05) and with the Schirmer's test in patients with NAIDED (r = -0.24, P < 0.05). However, the IVCM-NMsc was positively correlated with the Oxford score only in patients with AIDED (r = 0.3, P < 0.05).

Conclusions

The proposed IVCM scoring system showed significant correlations with clinical parameters along with conjunctival HLA-DR quantification in patients with DED.

Translational Relevance

The IVCM grading score represents an interesting point of commonality among clinical parameters, imaging, and molecular investigation of the ocular surface.

Objective Analysis of Corneal Nerves and Dendritic Cells

Corneal nerves and dendritic cells are increasingly being visualised to serve as clinical parameters in the diagnosis of ocular surface diseases using intravital confocal microscopy. In this review, different methods of image analysis are presented. The use of deep learning algorithms, which enable automated pattern recognition, is explained in detail using our own developments and compared with other established methods.

Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review

Artificial intelligence (AI) has seen significant progress in medical diagnostics, particularly in image and video analysis. This review focuses on the application of AI in analyzing in vivo confocal microscopy (IVCM) images for corneal diseases. The cornea, as an exposed and delicate part of the body, necessitates the precise diagnoses of various conditions. Convolutional neural networks (CNNs), a key component of deep learning, are a powerful tool for image data analysis. This review highlights AI applications in diagnosing keratitis, dry eye disease, and diabetic corneal neuropathy. It discusses the potential of AI in detecting infectious agents, analyzing corneal nerve morphology, and identifying the subtle changes in nerve fiber characteristics in diabetic corneal neuropathy. However, challenges still remain, including limited datasets, overfitting, low-quality images, and unrepresentative training datasets. This review explores augmentation techniques and the importance of feature engineering to address these challenges. Despite the progress made, challenges are still present, such as the "black-box" nature of AI models and the need for explainable AI (XAI). Expanding datasets, fostering collaborative efforts, and developing user-friendly AI tools are crucial for enhancing the acceptance and integration of AI into clinical practice.

Potential applications of artificial intelligence in image analysis in cornea diseases: a review

Artificial intelligence (AI) is an emerging field which could make an intelligent healthcare model a reality and has been garnering traction in the field of medicine, with promising results. There have been recent developments in machine learning and/or deep learning algorithms for applications in ophthalmology-primarily for diabetic retinopathy, and age-related macular degeneration. However, AI research in the field of cornea diseases is relatively new. Algorithms have been described to assist clinicians in diagnosis or detection of cornea conditions such as keratoconus, infectious keratitis and dry eye disease. AI may also be used for segmentation and analysis of cornea imaging or tomography as an adjunctive tool. Despite the potential advantages that these new technologies offer, there are challenges that need to be addressed before they can be integrated into clinical practice. In this review, we aim to summarize current literature and provide an update regarding recent advances in AI technologies pertaining to corneal diseases, and its potential future application, in particular pertaining to image analysis.

Open-Source Automated Segmentation of Neuronal Structures in Corneal Confocal Microscopy Images of the Subbasal Nerve Plexus With Accuracy on Par With Human Segmentation

PURPOSE

The aim of this study was to perform automated segmentation of corneal nerves and other structures in corneal confocal microscopy (CCM) images of the subbasal nerve plexus (SNP) in eyes with ocular surface diseases (OSDs).

METHODS

A deep learning-based 2-stage algorithm was designed to perform segmentation of SNP features. In the first stage, to address applanation artifacts, a generative adversarial network-enabled deep network was constructed to identify 3 neighboring corneal layers on each CCM image: epithelium, SNP, and stroma. This network was trained/validated on 470 images of each layer from 73 individuals. The segmented SNP regions were further classified in the second stage by another deep network as follows: background, nerve, neuroma, and immune cells. Twenty-one-fold cross-validation was used to assess the performance of the overall algorithm on a separate data set of 207 manually segmented SNP images from 43 patients with OSD.

RESULTS

For the background, nerve, neuroma, and immune cell classes, the Dice similarity coefficients of the proposed automatic method were 0.992, 0.814, 0.748, and 0.736, respectively. The performance metrics for automatic segmentations were statistically better or equal as compared to human segmentation. In addition, the resulting clinical metrics had good to excellent intraclass correlation coefficients between automatic and human segmentations.

CONCLUSIONS

The proposed automatic method can reliably segment potential CCM biomarkers of OSD onset and progression with accuracy on par with human gradings in real clinical data, which frequently exhibited image acquisition artifacts. To facilitate future studies on OSD, we made our data set and algorithms freely available online as an open-source software package.

The impact of dry eye disease on corneal nerve parameters: A systematic review and meta-analysis

PURPOSE

Dry eye disease (DED) is a growing global health problem with a significant impact on the quality of life of patients. While neurosensory abnormalities have been recognised as a contributor to DED pathophysiology, the potential role of in vivo corneal confocal microscopy in detecting nerve loss or damage remains unclear. This systematic review with meta-analysis (PROSPERO registered CRD42022381861) investigated whether DED has an impact on sub-basal corneal nerve parameters.

METHODS

PubMed, Embase and Web of Science Core Collection databases were searched from inception to 9 December 2022. Studies using laser scanning confocal microscopy to compare corneal nerve parameters of DED with healthy eyes were included. Study selection process and data extraction were performed by two independent members of the review team.

RESULTS

Twenty-two studies with 916 participants with DED and 491 healthy controls were included, with 21 of these studies included in subsequent meta-analyses. There was a decrease in total corneal nerve length (-3.85 mm/mm2 ; 95% CI -5.16, -2.55), corneal main nerve trunk density (-4.81 number/mm2 ; 95% CI -7.94, -1.68) and corneal nerve branch density (-15.52 number/mm2 ; 95% CI -27.20, -3.84) in DED eyes compared with healthy eyes, with subgroup analysis demonstrating that these differences were more evident in studies using NeuronJ software, a semi-automated procedure. While this review found evidence of loss of corneal nerve parameters in eyes with DED compared with healthy controls, particularly with the use of a semi-automated image analysis method, it is evident that there is substantial heterogeneity between studies in terms of corneal nerve imaging methodology.

CONCLUSIONS

Standardisation is required in terms of terminology and analysis, with more research needed to potentially improve the clinical applicability and practicality of corneal nerve imaging. Further investigation is also required to confirm the diagnostic accuracy of this imaging modality and its potential for monitoring DED treatment efficacy.

Automated identification and quantification of activated dendritic cells in central cornea using artificial intelligence

PURPOSE

To validate an algorithm quantifying activated dendritic cells (aDCs) using in-vivo confocal microscopy (IVCM) images.

METHODS

IVCM images obtained at the Miami Veterans Affairs Hospital were retrospectively analyzed. ADCs were quantified both with an automated algorithm and manually. Intra-class-correlation (ICC) and a Bland-Altman plot were used to compare automated and manual counts. As a secondary analysis, individuals were grouped by Dry Eye (DE) subtype: 1) aqueous-tear deficiency (ATD; Schirmer's test ≤5 mm); 2) evaporative DE (EDE; TBUT≤5s); or 3) control (Schirmer's test>5 mm; TBUT>5s) and ICCs were re-examined.

RESULTS

173 non-overlapping images from 86 individuals were included in this study. The mean age was 55.2 ± 16.7 years; 77.9% were male; 20 had ATD; 18 EDE and 37 were controls. The mean number of aDCs in the central cornea quantified automatically was 0.83 ± 1.33 cells/image and manually was 1.03 ± 1.65 cells/image. A total of 143 aDCs were identified by the automated algorithm and 178 aDCs were identified manually. While a Bland-Altman plot indicated a small difference between the two methods (0.19, p < 0.01), the ICC of 0.80 (p = 0.01) demonstrated excellent agreement. Secondarily, similar results were found by DE type with an ICC of 0.75 (p = 0.01) for the ATD group, 0.80 (p = 0.01) for EDE, and 0.82 (p = 0.01) for controls.

CONCLUSIONS

Quantification of aDCs within the central cornea may be successfully estimated using an automated machine learning based algorithm. While this study suggests that analysis using artificial intelligence has comparable results with manual quantification, further longitudinal research to validate our findings in more diverse populations may be warranted.

In-vivo corneal confocal microscopy: Imaging analysis, biological insights and future directions

In-vivo corneal confocal microscopy is a powerful imaging technique which provides clinicians and researcher with the capabilities to observe microstructures at the ocular surfaces in significant detail. In this Mini Review, the optics and image analysis methods with the use of corneal confocal microscopy are discussed. While novel insights of neuroanatomy and biology of the eyes, particularly the ocular surface, have been provided by corneal confocal microscopy, some debatable elements observed using this technique remain and these are explored in this Mini Review. Potential improvements in imaging methodology and instrumentation are also suggested.

Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images

Objective

In order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians' workload.

Methods

A total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman's membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance.

Results

The accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman's membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.

Conclusion

A computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes.

Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease

Dry eye disease (DED) is one of the most common diseases worldwide that can lead to a significant impairment of quality of life. The diagnosis and treatment of the disease are often challenging because of the lack of correlation between the signs and symptoms, limited reliability of diagnostic tests, and absence of established consensus on the diagnostic criteria. The advancement of machine learning, particularly deep learning technology, has enabled the application of artificial intelligence (AI) in various anterior segment disorders, including DED. Currently, many studies have reported promising results of AI-based algorithms for the accurate diagnosis of DED and precise and reliable assessment of data obtained by imaging devices for DED. Thus, the integration of AI into clinical approaches for DED can enhance diagnostic and therapeutic performance. In this review, in addition to a brief summary of the application of AI in anterior segment diseases, we will provide an overview of studies regarding the application of AI in DED and discuss the recent advances in the integration of AI into the clinical approach for DED.