Multispectral imaging: Review of current applications

Differential diagnosis of myopic choroidal neovascularization (mCNV): insights from multimodal imaging and treatment implications

PURPOSE

The aim of this article is to conduct a comprehensive systematic review about the current understandings and differential diagnosis of myopic choroidal neovascularization (mCNV) and other several similar diseases, describing their multimodal imaging analysis, prognostic implications, and current types of management.

METHODS

This systematic review was performed based on a search on the PubMed database of relevant papers regarding mCNV and other entities discussed in the paper, according to our current knowledge.

RESULTS

Through the integration of a multimodal imaging approach, especially optical coherence tomography (OCT), along with accurate demographic and clinical assessment, it becomes possible to effectively differentiate mCNV from similar yet heterogeneous entities. These conditions include macular hemorrhage due to new lacquer crack (LC) formation, inflammatory diseases such as punctate inner choroidopathy (PIC)/multifocal choroidits (MFC) and epiphenomenon multiple evanescent white dot syndrome (Epi-MEWDS), neovascular age-related macular degeneration (nAMD), idiopathic CNV (ICNV), dome-shaped macula (DSM) with subretinal fluid, retinal pigment epithelium (RPE) humps, angioid streaks (AS), choroidal rupture (CR), and choroidal osteoma (CO). Each one of these entities will be described and discussed in this article.

CONCLUSION

Myopic choroidal neovascularization is a common retinal condition, especially among young individuals. Accurate diagnosis and differentiation from similar conditions are crucial for effective treatment. Multimodal imaging, particularly OCT, plays a crucial role in precise assessment. Future research should focus on defining biomarkers and distinguishing features to facilitate prompt treatment.

High-accuracy heart rate detection using multispectral IPPG technology combined with a deep learning algorithm

Image Photoplethysmography (IPPG) technology is a noncontact physiological parameter detection technology, which has been widely used in heart rate (HR) detection. However, traditional imaging devices still have issues such as narrower receiving spectral range and inferior motion detection performance. In this paper, we propose a HR detection method based on multi-spectral video. Our method combining multispectral imaging with IPPG technology provides more accurate physiological information. To realize real-time evaluation of HR directly from facial multispectral videos, we propose a new end-to-end neural network, namely IPPGResNet18. The IPPGResNet18 model was trained on the multispectral video dataset from which better results were achieved: MAE = 2.793, RMSE = 3.695, SD = 3.707, p = 0.304. The experimental results demonstrate a high accuracy of HR detection under motion state using this detection method. In respect of real-time monitoring of HR during movement, our method is obviously superior to the conventional technical solutions.

A Multi-Shot Approach for Spatial Resolution Improvement of Multispectral Images from an MSFA Sensor

Multispectral imaging technology has advanced significantly in recent years, allowing single-sensor cameras with multispectral filter arrays to be used in new scene acquisition applications. Our camera, developed as part of the European CAVIAR project, uses an eight-band MSFA to produce mosaic images that can be decomposed into eight sparse images. These sparse images contain only pixels with similar spectral properties and null pixels. A demosaicing process is then applied to obtain fully defined images. However, this process faces several challenges in rendering fine details, abrupt transitions, and textured regions due to the large number of null pixels in the sparse images. Therefore, we propose a sparse image composition method to overcome these challenges by reducing the number of null pixels in the sparse images. To achieve this, we increase the number of snapshots by simultaneously introducing a spatial displacement of the sensor by one to three pixels on the horizontal and/or vertical axes. The set of snapshots acquired provides a multitude of mosaics representing the same scene with a redistribution of pixels. The sparse images from the different mosaics are added together to get new composite sparse images in which the number of null pixels is reduced. A bilinear demosaicing approach is applied to the composite sparse images to obtain fully defined images. Experimental results on images projected onto the response of our MSFA filter show that our composition method significantly improves image spatial resolution and minimizes reconstruction errors while preserving spectral fidelity.

THE DIAGNOSTIC VALUE OF MULTISPECTRAL FUNDUS IMAGING IN HYPERTENSIVE RETINOPATHY

PURPOSE

To observe the diagnostic value of multispectral fundus imaging (MSI) in hypertensive retinopathy (HR).

METHODS

A total of 100 patients with HR were enrolled in this cross-sectional study, and all participants received fundus photography and MSI. Participants with severe HR received fundus fluorescein angiography (FFA). The diagnostic consistency between fundus photography and MSI in the diagnosis of HR was calculated. The sensitivity of MSI in the diagnosis of severe HR was calculated by comparison with FFA. Choroidal vascular index was calculated in patients with HR using MSI at 780 nm.

RESULTS

MSI and fundus photography were highly concordant in the diagnosis of HR with a Kappa value = 0.883. MSI had a sensitivity of 96% in diagnosing retinal hemorrhage, a sensitivity of 89.47% in diagnosing retinal exudation, a sensitivity of 100% in diagnosing vascular compression indentation, and a sensitivity of 96.15% in diagnosing retinal arteriosclerosis. The choroidal vascular index of the patients in the HR group was significantly lower than that of the control group, whereas there was no significant difference between the affected and fellow eyes.

CONCLUSION

As a noninvasive modality of observation, MSI may be a new tool for the diagnosis and assessment of HR.

Colour fusion effect on deep learning classification of uveal melanoma

BACKGROUND

Reliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. The purpose of this study is to validate deep learning classification of uveal melanoma and choroidal nevi, and to evaluate the effect of colour fusion options on the classification performance.

METHODS

A total of 798 ultra-widefield retinal images of 438 patients were included in this retrospective study, comprising 157 patients diagnosed with UM and 281 patients diagnosed with choroidal naevus. Colour fusion options, including early fusion, intermediate fusion and late fusion, were tested for deep learning image classification with a convolutional neural network (CNN). F1-score, accuracy and the area under the curve (AUC) of a receiver operating characteristic (ROC) were used to evaluate the classification performance.

RESULTS

Colour fusion options were observed to affect the deep learning performance significantly. For single-colour learning, the red colour image was observed to have superior performance compared to green and blue channels. For multi-colour learning, the intermediate fusion is better than early and late fusion options.

CONCLUSION

Deep learning is a promising approach for automated classification of uveal melanoma and choroidal nevi. Colour fusion options can significantly affect the classification performance.

Multispectral Fundus Photography of Choroidal Nevi With Trans-Palpebral Illumination

Purpose

The purpose of this study was to investigate the spectral characteristics of choroidal nevi and assess the feasibility of quantifying the basal diameter of choroidal nevi using multispectral fundus images captured with trans-palpebral illumination.

Methods

The study used a widefield fundus camera with multispectral (625 nm, 780 nm, 850 nm, and 970 nm) trans-palpebral illumination to examine eight subjects diagnosed with choroidal nevi. Geometric features of nevi, including border clarity, overlying drusen, and lesion basal diameter, were characterized. Clinical imagers, including scanning laser ophthalmoscopy (SLO), autofluorescence (AF), and optical coherence tomography (OCT), were utilized for comparative assessment.

Results

Fundus images depicted nevi as dark regions with high contrast against the background. Near-infrared (NIR) fundus images provided enhanced visibility of lesion borders compared to visible fundus images and SLO images. Lesion-background contrast measurements revealed 635 nm SLO at 11% and 625 nm fundus at 42%. Significantly enhanced contrasts were observed in NIR fundus images at 780 nm (73%), 850 nm (63%), and 970 nm (67%). For quantifying the diameter of nevi, NIR fundus images at 780 nm and 850 nm yielded a deviation of less than 10% when compared to OCT measurements.

Conclusions

NIR fundus photography with trans-palpebral illumination enhances nevi visibility and boundary definition compared to SLO. Agreement in diameter measurements with OCT validates the accuracy and reliability of this method for choroidal nevi assessment.

Translational Relevance

Multispectral fundus imaging with trans-palpebral illumination improves choroidal nevi visibility and accurately measures basal diameter, promising to enhance clinical practices in screening, diagnosis, and monitoring of choroidal nevi.

Multispectral Fundus Photography of Choroidal Nevi with Trans-Palpebral Illumination

Purpose

To investigate the spectral characteristics of choroidal nevi and assess the feasibility of quantifying the basal diameter of choroidal nevi using multispectral fundus images captured with trans-palpebral illumination.

Methods

The study employed a widefield fundus camera with multispectral (625 nm, 780 nm, 850 nm, and 970 nm) trans-palpebral illumination. Geometric features of choroidal nevi, including border clarity, overlying drusen, and lesion basal diameter, were characterized. Clinical imagers, including scanning laser ophthalmoscopy (SLO), autofluorescence (AF), and optical coherence tomography (OCT), were utilized for comparative assessment.

Results

Fundus images captured with trans-palpebral illumination depicted nevi as dark regions with high contrast against the background. Near-infrared (NIR) fundus images provided enhanced visibility of lesion borders compared to visible light fundus images and SLO images. Lesion-background contrast measurements revealed 635 nm SLO at 11% and 625 nm fundus at 42%. Significantly enhanced contrasts were observed in NIR fundus images at 780 nm (73%), 850 nm (63%), and 970 nm (67%). For quantifying the basal diameter of nevi, NIR fundus images at 780 nm and 850 nm yielded a deviation of less than 10% when compared to OCT B-scan measurements.

Conclusion

NIR fundus photography with trans-palpebral illumination enhances nevi visibility and boundary definition compared to SLO. Agreement in basal diameter measurements with OCT validates the accuracy and reliability of this method for choroidal nevi assessment.

Enteroscopy versus Video Capsule Endoscopy for Automatic Diagnosis of Small Bowel Disorders-A Comparative Analysis of Artificial Intelligence Applications

BACKGROUND

Small bowel disorders present a diagnostic challenge due to the limited accessibility of the small intestine. Accurate diagnosis is made with the aid of specific procedures, like capsule endoscopy or double-ballon enteroscopy, but they are not usually solicited and not widely accessible. This study aims to assess and compare the diagnostic effectiveness of enteroscopy and video capsule endoscopy (VCE) when combined with artificial intelligence (AI) algorithms for the automatic detection of small bowel diseases.

MATERIALS AND METHODS

We performed an extensive literature search for relevant studies about AI applications capable of identifying small bowel disorders using enteroscopy and VCE, published between 2012 and 2023, employing PubMed, Cochrane Library, Google Scholar, Embase, Scopus, and ClinicalTrials.gov databases.

RESULTS

Our investigation discovered a total of 27 publications, out of which 21 studies assessed the application of VCE, while the remaining 6 articles analyzed the enteroscopy procedure. The included studies portrayed that both investigations, enhanced by AI, exhibited a high level of diagnostic accuracy. Enteroscopy demonstrated superior diagnostic capability, providing precise identification of small bowel pathologies with the added advantage of enabling immediate therapeutic intervention. The choice between these modalities should be guided by clinical context, patient preference, and resource availability. Studies with larger sample sizes and prospective designs are warranted to validate these results and optimize the integration of AI in small bowel diagnostics.

CONCLUSIONS

The current analysis demonstrates that both enteroscopy and VCE with AI augmentation exhibit comparable diagnostic performance for the automatic detection of small bowel disorders.