A Novel Device for Smartphone-Based Fundus Imaging and Documentation in Clinical Practice: Comparative Image Analysis Study

Evaluating imaging repeatability of fully self-service fundus photography within a community-based eye disease screening setting

PURPOSE

This study aimed to investigate the imaging repeatability of self-service fundus photography compared to traditional fundus photography performed by experienced operators.

DESIGN

Prospective cross-sectional study.

METHODS

In a community-based eye diseases screening site, we recruited 65 eyes (65 participants) from the resident population of Shanghai, China. All participants were devoid of cataract or any other conditions that could potentially compromise the quality of fundus imaging. Participants were categorized into fully self-service fundus photography or traditional fundus photography group. Image quantitative analysis software was used to extract clinically relevant indicators from the fundus images. Finally, a statistical analysis was performed to depict the imaging repeatability of fully self-service fundus photography.

RESULTS

There was no statistical difference in the absolute differences, or the extents of variation of the indicators between the two groups. The extents of variation of all the measurement indicators, with the exception of the optic cup area, were below 10% in both groups. The Bland-Altman plots and multivariate analysis results were consistent with results mentioned above.

CONCLUSIONS

The image repeatability of fully self-service fundus photography is comparable to that of traditional fundus photography performed by professionals, demonstrating promise in large-scale eye disease screening programs.

Mobile technologies for rehabilitation in non-specific spinal disorders: a systematic review of the efficacy and potential for implementation in low- and middle-income countries

PURPOSE

The aim of this systematic review was primarily to identify the types of mHealth technologies for the rehabilitation of non-specific spinal disorders, second to evaluate their efficacy, and finally to determine their applicability in LMICs.

METHODS

Three databases (Scopus, PubMed, and Web of Science) were searched for randomized controlled trials and clinical trials from January 2012 until December 2022. Studies were found eligible when using mHealth technologies for the rehabilitation of non-specific spinal disorders. To evaluate efficacy, the primary outcome was pain intensity, and the secondary outcomes were disability and quality of life. To evaluate the applicability in LMICs, information about financial and geographical accessibility, offline usability, and languages was extracted.

RESULTS

Fifteen studies were included comprising 1828 participants who suffer from non-specific low back pain (86.05%) and non-specific neck pain (13.95%). Fourteen distinct smartphone-based interventions and two sensor system interventions were found, with a duration ranging from four weeks to six months. All mHealth interventions demonstrated efficacy for the improvement of pain, disability and quality of life in non-specific spinal disorders, particularly low back pain. Five of the evaluated smartphone applications were free of charge accessible and had language features that could be adapted for use in LMICs.

CONCLUSION

mHealth interventions can be used and integrated into the conventional treatment of non-specific spinal disorders in rehabilitation. They have demonstrated efficacy and could be implemented in LMICs with minor adaptations to overcome language barriers and the absolute necessity of the internet.

A New Vessel-Based Method to Estimate Automatically the Position of the Nonfunctional Fovea on Altered Retinography From Maculopathies

Purpose

The purpose of this study was to validate a new automated method to locate the fovea on normal and pathological fundus images. Compared to the normative anatomic measures (NAMs), our vessel-based fovea localization (VBFL) approach relies on the retina's vessel structure to make predictions.

Methods

The spatial relationship between the fovea location and vessel characteristics is learnt from healthy fundus images and then used to predict fovea location in new images. We evaluate the VBFL method on three categories of fundus images: healthy images acquired with different head orientations and fixation locations, healthy images with simulated macular lesions, and pathological images from age-related macular degeneration (AMD).

Results

For healthy images taken with the head tilted to the side, the NAM estimation error is significantly multiplied by 4, whereas VBFL yields no significant increase, representing a 73% reduction in prediction error. With simulated lesions, VBFL performance decreases significantly as lesion size increases and remains better than NAM until lesion size reaches 200 degrees2. For pathological images, average prediction error was 2.8 degrees, with 64% of the images yielding an error of 2.5 degrees or less. VBFL was not robust for images showing darker regions and/or incomplete representation of the optic disk.

Conclusions

The vascular structure provides enough information to precisely locate the fovea in fundus images in a way that is robust to head tilt, eccentric fixation location, missing vessels, and actual macular lesions.

Translational Relevance

The VBFL method should allow researchers and clinicians to assess automatically the eccentricity of a newly developed area of fixation in fundus images with macular lesions.

Ophthalmic imaging in abusive head trauma

BACKGROUND

Ophthalmic imaging plays an increasingly important role the evaluation of abusive head trauma, however these imaging modalities may be unfamiliar to non-ophthalmologists.

OBJECTIVE

To provide pediatricians and child abuse pediatric professionals with background on ophthalmic imaging techniques in the context of suspected abuse, as well as information on commercial options and costs for those interested in augmenting their ophthalmic imaging capabilities.

METHODS

We performed a review of the ophthalmic imaging literature for fundus photography, ocular coherence tomography, fluorescein angiography, ocular ultrasound, computed tomography, magnetic resonance imaging and postmortem imaging. We also contacted individual vendors for equipment pricing information.

RESULTS

For each ophthalmic imaging modality, we demonstrate its role in the evaluation of abusive head trauma including indications, potential findings, sensitivity and specificity of findings for abuse, and commercial options.

CONCLUSIONS

Ophthalmic imaging is an important supportive component of the evaluation for abusive head trauma. When used in conjunction with clinical examination, ophthalmic imaging can improve diagnostic accuracy, support documentation, and possibly improve communication in medicolegal contexts.

Use of multimodal dataset in AI for detecting glaucoma based on fundus photographs assessed with OCT: focus group study on high prevalence of myopia

BACKGROUND

Glaucoma is one of the major causes of blindness; it is estimated that over 110 million people will be affected by glaucoma worldwide by 2040. Research on glaucoma detection using deep learning technology has been increasing, but the diagnosis of glaucoma in a large population with high incidence of myopia remains a challenge. This study aimed to provide a decision support system for the automatic detection of glaucoma using fundus images, which can be applied for general screening, especially in areas of high incidence of myopia.

METHODS

A total of 1,155 fundus images were acquired from 667 individuals with a mean axial length of 25.60 ± 2.0 mm at the National Taiwan University Hospital, Hsinchu Br. These images were graded based on the findings of complete ophthalmology examinations, visual field test, and optical coherence tomography into three groups: normal (N, n = 596), pre-perimetric glaucoma (PPG, n = 66), and glaucoma (G, n = 493), and divided into a training-validation (N: 476, PPG: 55, G: 373) and test (N: 120, PPG: 11, G: 120) sets. A multimodal model with the Xception model as image feature extraction and machine learning algorithms [random forest (RF), support vector machine (SVM), dense neural network (DNN), and others] was applied.

RESULTS

The Xception model classified the N, PPG, and G groups with 93.9% of the micro-average area under the receiver operating characteristic curve (AUROC) with tenfold cross-validation. Although normal and glaucoma sensitivity can reach 93.51% and 86.13% respectively, the PPG sensitivity was only 30.27%. The AUROC increased to 96.4% in the N + PPG and G groups. The multimodal model with the N + PPG and G groups showed that the AUROCs of RF, SVM, and DNN were 99.56%, 99.59%, and 99.10%, respectively; The N and PPG + G groups had less than 1% difference. The test set showed an overall 3%-5% less AUROC than the validation results.

CONCLUSION

The multimodal model had good AUROC while detecting glaucoma in a population with high incidence of myopia. The model shows the potential for general automatic screening and telemedicine, especially in Asia.

TRIAL REGISTRATION

The study was approved by the Institutional Review Board of the National Taiwan University Hospital, Hsinchu Branch (no. NTUHHCB 108-025-E).

Emerging Trends in Smartphone Photo Documentation of Child Physical Abuse

ABSTRACT

Photo documentation of injuries on children is universally recommended in cases of suspected child physical abuse. As technology improves, the ability to document physical examination findings through smartphone photography is increasingly accessible and practical. The quality of images captured on smartphones now rivals traditional photography and the integration of photo capture within the electronic medical record has led to a variety of fields adopting smartphone photo documentation for diagnosis, consult, and follow-up. However, in cases of child physical abuse, practitioners have been hesitant to adopt smartphones as a primary means of photo documentation because of concerns around image quality, privacy, and security. In this article, we discuss the technology of available smartphone cameras and current evidence regarding their use for photo documentation, use existing guidelines to propose a workflow to improve the yield of smartphone photo documentation in child physical abuse, and discuss common medicolegal concerns.

Smartphone-Enabled versus Conventional Otoscopy in Detecting Middle Ear Disease: A Meta-Analysis

Traditional otoscopy has some limitations, including poor visualization and inadequate time for evaluation in suboptimal environments. Smartphone-enabled otoscopy may improve examination quality and serve as a potential diagnostic tool for middle ear diseases using a telemedicine approach. The main objectives are to compare the correctness of smartphone-enabled otoscopy and traditional otoscopy and to evaluate the diagnostic confidence of the examiner via meta-analysis. From inception through 20 January 2022, the Cochrane Library, PubMed, EMBASE, Web of Science, and Scopus databases were searched. Studies comparing smartphone-enabled otoscopy with traditional otoscopy regarding the outcome of interest were eligible. The relative risk (RR) for the rate of correctness in diagnosing ear conditions and the standardized mean difference (SMD) in diagnostic confidence were extracted. Sensitivity analysis and trial sequential analyses (TSAs) were conducted to further examine the pooled results. Study quality was evaluated by using the revised Cochrane risk of bias tool 2. Consequently, a total of 1840 examinees were divided into the smartphone-enabled otoscopy group and the traditional otoscopy group. Overall, the pooled result showed that smartphone-enabled otoscopy was associated with higher correctness than traditional otoscopy (RR, 1.26; 95% CI, 1.06 to 1.51; p = 0.01; I² = 70.0%). Consistently significant associations were also observed in the analysis after excluding the simulation study (RR, 1.10; 95% CI, 1.00 to 1.21; p = 0.04; I² = 0%) and normal ear conditions (RR, 1.18; 95% CI, 1.01 to 1.40; p = 0.04; I² = 65.0%). For the confidence of examiners using both otoscopy methods, the pooled result was nonsignificant between the smartphone-enabled otoscopy and traditional otoscopy groups (SMD, 0.08; 95% CI, -0.24 to 0.40; p = 0.61; I² = 16.3%). In conclusion, smartphone-enabled otoscopy was associated with a higher rate of correctness in the detection of middle ear diseases, and in patients with otologic complaints, the use of smartphone-enabled otoscopy may be considered. More large-scale studies should be performed to consolidate the results.

[Smartphone-based fundus imaging: applications and adapters]

BACKGROUND

Smartphone-based fundus imaging (SBFI) is an innovative and low-cost alternative for color fundus photography. Since the first reports on this topic more than 10 years ago a large number of studies on different adapters and clinical applications have been published.

OBJECTIVE

The aim of this review article is to provide an overview on the development of SBFI and adapters and clinical applications published so far.

MATERIAL AND METHODS

A literature search was performed using the MEDLINE and Science Citation Index Expanded databases without time restrictions.

RESULTS

Overall, 11 adapters were included and compared in terms of exemplary image material, field of view, acquisition costs, weight, software, application range, smartphone compatibility and certification. Previously published SBFI applications are screening for diabetic retinopathy, glaucoma and retinopathy of prematurity as well as the application in emergency medicine, pediatrics and medical education/teaching. Image quality of conventional retinal cameras is in general superior to SBFI. First approaches on automatic detection of diabetic retinopathy through SBFI are promising and the use of automatic image processing algorithms enables the generation of wide-field image montages.

CONCLUSION

SBFI is a versatile, mobile, low-cost alternative to conventional equipment for color fundus photography. In addition, it facilitates the delegation of ophthalmological examinations to assistance personnel in telemedical settings, could simplify retinal documentation, improve teaching, and improve ophthalmological care, particularly in countries with low and middle incomes.

Clinical Role of Smartphone Fundus Imaging in Diabetic Retinopathy and Other Neuro-retinal Diseases

Purpose: In today's life, many electronic gadgets have the potential to become invaluable health care devices in future. The gadgets in this category include smartphones, smartwatches, and others. Till now, smartphone role has been highlighted on many occasions in different areas, and they continue to possess immense role in clinical documentation, clinical consultation, and digitalization of ocular care. In last one decade, many treatable conditions including diabetic retinopathy, glaucoma, and other pediatric retinal diseases are being imaged using smartphones.Methods: To comprehend this cumulative knowledge, a detailed medical literature search was conducted on PubMed/Medline, Scopus, and Web of Science till February 2021.Results: The included literature revealed a definitive progress in posterior segment imaging. From simple torch light with smartphone examination to present day compact handy devices with artificial intelligence integrated software's have changed the very perspectives of ocular imaging in ophthalmology. The consistently reproducible results, constantly improving imaging techniques, and most importantly their affordable costs have renegotiated their role as effective screening devices in ophthalmology. Moreover, the obtained field of view, ocular safety, and their key utility in non-ophthalmic specialties are also growing.Conclusions: To conclude, smartphone imaging can now be considered as a quick, cost-effective, and digitalized tool for posterior segment screenings, however, their definite role in routine ophthalmic clinics is yet to be established.

The Role of Telemedicine, In-Home Testing and Artificial Intelligence to Alleviate an Increasingly Burdened Healthcare System: Diabetic Retinopathy

In the presence of the ever-increasing incidence of diabetes mellitus (DM), the prevalence of diabetic eye disease (DED) is also growing. Despite many improvements in diabetic care, DM remains a leading cause of visual impairment in working-age patients. So far, prevention has been the best way to protect vision. The sooner we diagnose DED, the more effective the treatment is. Thus, diabetic retinopathy (DR) screening, especially with imaging techniques, is a method of choice for vision protection. To alleviate the burden of diabetic patients who need ophthalmic care, telemedicine and in-home testing are used, supported by artificial intelligence (AI) algorithms. This is why we decided to evaluate current image teleophthalmology methods used for DR screening. We searched the PubMed platform for papers published over the last 5 years (2015–2020) using the following key words: telemedicine in diabetic retinopathy screening, diabetic retinopathy screening, automated diabetic retinopathy screening, artificial intelligence in diabetic retinopathy screening, smartphone diabetic retinopathy testing. We have included 118 original articles meeting the above criteria, discussing imaging diabetic retinopathy screening methods. We have found that fundus cameras, stable or mobile, are most commonly used for retinal photography, with portable fundus cameras also relatively common. Other possibilities involve the use of ultra-wide-field (UWF) imaging and even optical coherence tomography (OCT) devices for DR screening. Also, the role of smartphones is increasingly recognized in the field. Retinal fundus images are assessed by humans instantly or remotely, while AI algorithms seem to be useful tools facilitating retinal image assessment. The common use of smartphones and availability of relatively cheap, easy-to-use adapters for retinal photographs augmented by AI algorithms make it possible for eye fundus photographs to be taken by non-specialists and in non-medical setting. This opens the way for in-home testing conducted on a much larger scale in the future. In conclusion, based on current DR screening techniques, we can suggest that the future practice of eye care specialists will be widely supported by AI algorithms, and this way will be more effective.

Learning curve evaluation upskilling retinal imaging using smartphones

Smartphone-based fundus imaging (SBFI) is a low-cost approach for screening of various ophthalmic diseases and particularly suited to resource limited settings. Thus, we assessed how best to upskill alternative healthcare cadres in SBFI and whether quality of obtained images is comparable to ophthalmologists. Ophthalmic assistants and ophthalmologists received a standardized training to SBFI (Heine iC2 combined with an iPhone 6) and 10 training examinations for capturing central retinal images. Examination time, total number of images, image alignment, usable field-of-view, and image quality (sharpness/focus, reflex artifacts, contrast/illumination) were analyzed. Thirty examiners (14 ophthalmic assistants and 16 ophthalmologists) and 14 volunteer test subjects were included. Mean examination time (1st and 10th training, respectively: 2.17 ± 1.54 and 0.56 ± 0.51 min, p < .0001), usable field-of-view (92 ± 16% and 98 ± 6.0%, p = .003) and image quality in terms of sharpness/focus (p = .002) improved by the training. Examination time was significantly shorter for ophthalmologists compared to ophthalmic assistants (10th training: 0.35 ± 0.21 and 0.79 ± 0.65 min, p = .011), but there was no significant difference in usable field-of-view and image quality. This study demonstrates the high learnability of SBFI with a relatively short training and mostly comparable results across healthcare cadres. The results will aid implementing and planning further SBFI field studies.