The Advancement of Amblyopia Diagnosis and Therapeutic Intervention: A Comprehensive Approach, Emphasizing Integration of Advanced Systems and Neuroplasticity

ASHISH CHETTIMADA

he/him | age 15 | Waterloo, ON

MILSET Gold Medal, Top Delegation Award, and representative of Team Canada, 2023 MILSET International Science Expo | Gold Excellence Award, S.M. Blair Family Foundation Award, and Youth Can Innovate National Grand Award, Canada-Wide Science Fair | Gold Medallion and Award of Merit, WWSEF Science Fair

Edited by Riya Gandhi


INTRODUCTION

Affecting 1 out of every 20 people, amblyopia (or lazy eye) is a neurodevelopmental disorder that constitutes the largest threat to the vision of children and is caused by abnormal visual pathway development in early childhood (Li et al., 2020). Risk factors in amblyopia can be divided into ocular and non-ocular categories, but based on current evidence, amblyopia is mainly caused by uncorrected refractive error, strabismus, cataract, and ptosis (Zagui et al., 2019). There are a plethora of visual function deficits experienced by the amblyopic individual that could impact learning, daily activities, and the psychological state of affected children (Birch et al., 2019). Therefore, it is essential to get the best treatment during the critical period to avoid severe consequences due to this disorder. Notably, amblyopia exhibits heightened responsiveness to therapeutic interventions in children under the age of 8 (Holmes et al., 2018) (Figure 1).

Figure 1:  Relationship between age and amblyopic eye visual acuity improvement. In children 3 to less than 13 years of age with moderate amblyopia (20/40 to 20/100, n = 829, A) or severe amblyopia (20/125 to 20/400, n = 167, B) from a meta-analysis of 4 amblyopia treatment trials ( Holmes et al., 2018)

Currently, the prevailing approach for addressing amblyopia predominantly involves patching as a primary treatment modality (Papageorgiou et al., 2019). With a high prevalence, amblyopia affects around 400 million people worldwide, and only 15% of children with amblyopia are officially diagnosed with it (Koo et al., 2017). This disparity underscores potential underdiagnosis and emphasizes the need for heightened awareness and early detection strategies in addressing this prevalent neurodevelopmental visual disorder.

To address this global issue, ReAlign was developed. It is a web application that can both diagnose and treat amblyopia in young individuals using new advancements in technology. ReAlign allows for easy accessibility and use for children worldwide (Figure 2).

Figure 2: ReAlign’s app features. ReAlign was designed to maximize gamification, accessibility, and personalization.

MATERIALS & METHODS

The initial phase in the development of ReAlign involved conducting interviews with optometrists to establish the criteria for discerning positive and negative diagnoses. Interviews were also conducted to help create the virtual adaptation of the Snellen Chart. Furthermore, delineations of severity ranges for amblyopia were created, allowing the user to receive a level of amblyopia with their diagnosis (Table 1).

Table 1: Severity and Visual Acuity Ranges of Amblyopia. This table shows the different severity ranges of amblyopia and the different visual acuity ranges for the amblyopic eye.

Currently, the following standardized criteria were set to avoid potential confounds:

ReAlign actively develops and integrates facial recognition software to mitigate the aforementioned limitations. This enhancement aims to offer users a more flexible and unrestricted interaction with the application, independent of specific hardware and setup constraints.

The app has two main components: the diagnostic exam and ReAlign treatment exercises. All users are required to first complete the diagnostic exam. In this exam, ReAlign works to create a virtual Snellen Chart. Users are prompted to go through the chart and say the letters they see on the screen out loud, and the voice recognition system will pick it up. From there, AI models use pre-loaded data and formulate an appropriate diagnosis (Figure 3). 

When the ReAlign exercises option is chosen, then users are prompted to complete exercises that correlate with both their age and activity preferences. While simultaneously patching the better-working eye and doing the exercise, the brain is forced to create stronger neural connections with the amblyopic eye. 

Figure 3: ReAlign App Details. This figure shows a simplified version of how both aspects of the app work (Diagnostic & Management). All the steps shown above are reviewed by health professionals to confirm a diagnosis’s credibility. Additionallu, the app will inform the user that any information provided is not a final professional opinion/diagnosis and that the user should reach out to professionals for further assessment.

DESIGN CRITERIA

A successful eye examination within the application is characterized by the accurate identification of amblyopia status through the integration of advanced systems and AI models. The approximate numerical diagnosis should be in the correct range as well. It should not provide inaccurate information/diagnoses, and this is unlikely due to the positive results gathered from preliminary beta testing.  

In the treatment aspect of the app, a successful result occurs when the visual acuity of the amblyopic eye has improved, and the score has increased. The improvement of the amblyopic eye is dependent on the severity range of amblyopia and the consistency in therapeutic exercises.

RESULTS

ReAlign’s diagnostic aspect has gone through three trials of preliminary beta testing and has demonstrated a notable improvement from 80.6% efficiency to 93.3% efficiency (Figure 4). 

Figure 4: Preliminary BETA Testing Results. These three figures above represent the progression of app efficiency (from left to right). Starting with an initial efficiency rate of 80.6^, the testing demonstrated sequential enhancement, culminating in a final efficiency rate of 93.3%. This trajectory illustrates the app’s performance optimization throughout the testing phases.

ReAlign’s diagnostic aspect has undergone three iterations of preliminary beta testing, revealing a noteworthy advancement in efficiency. Commencing with an initial efficacy rate of 80.6%, the subsequent trials culminated in a substantial enhancement, resulting in a final efficiency rate of 93.3%. The first trial included 31 participants, revealing multiple system errors and inaccuracies in amblyopia diagnoses and severity level assessments. After adjusting predefined parameters and data ranges, ReAlign moved to its second trial of testing, which gathered data from 33 new participants. Here the efficiency had a significant increase and demonstrated an 87.9% efficient product. Finally, after moving to the third trial, ReAlign’s diagnostic aspect showed 93.3% efficiency, and it eliminated incorrect diagnoses for severity levels and system failures. The comprehensive testing process amalgamated data from a total of 95 participants, providing a thorough assessment of ReAlign’s performance.

DISCUSSION

The results from sample testing the diagnostic aspect of the app showed that the productivity of the app has progressively increased, resulting in a diagnostic efficiency of 93.3%. The three sets of sample testing concluded the first round of testing, and the app will now move on to the next stage, where the productivity of more complex aspects will be assessed. The results retrieved from testing have shown what needs to be further improved and have also demonstrated future gateways for research and development. 

Based on the information provided, it appears that the specificity of the app for detecting amblyopia needs improvement. Specifically, the app is producing rare false positives. This suggests that the test is not adequately discriminating between those with the condition and those without, potentially due to issues with the current cut-off ranges used in the app. While this may not have a direct negative impact on the users, as they are not being missed for amblyopia diagnosis, it is important to address this issue to improve the overall accuracy and efficiency of the diagnostic process. To achieve this, it may be beneficial to adjust the set ranges for the diagnostic criteria in order to optimize the sensitivity and specificity of the test for detecting amblyopia.

FURTHER ADVANCEMENTS

Currently, the primary impediment to detecting amblyopia in children is their inability to comprehend the concept of optimal visual acuity because they aren’t aware of what “good vision” is. This leads to a lack of recognition that reduced visual acuity in one eye is a matter of concern. As a result, amblyopia is often not identified until it has progressed to a point where treatment is no longer effective, necessitating a greater emphasis on early detection and intervention.   

ReAlign has shown great promise for aiding in the diagnosis and possibly even the management of amblyopia. The results from preliminary testing showed that there are many gateways for further research. Following communication with researchers at the University of Chicago, it has been revealed that ReAlign, which is presently focused on leveraging neuroplasticity to treat amblyopia, may have the potential to expand its scope to address other neurodevelopmental disorders such as learning disabilities, attention deficit hyperactivity disorder (ADHD), and anxiety. The fundamental concept of neuroplasticity that underlies ReAlign's approach to amblyopia treatment may be directly relevant to the management of other conditions, thus providing an opportunity to broaden the impact of this novel therapeutic approach. Additionally, research on the potential for early treatment of amblyopia in infants is proposed, as this stage is characterized by a significant level of synapse formation (Povolo et al., 2019).

Lastly, consultation with Dr. Sahitya from the University of Chicago revealed ongoing research in related fields. She recommends investigating the use of deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS) for individuals with severe, untreated amblyopia who have exceeded the age limit for current treatment methods. DBS involves the implantation of electrodes into specific brain regions to regulate neural pathways, potentially enhancing visual processing (Figure 5).

Figure 5: Deep Brain Stimulation (DBS). DBS is a surgical procedure that can help some people with movement disorders. DBS is a minimally invasive procedure. First, surgeons place small electrodes in certain areas of your brain. About a week later, surgeons place an IPG (implanted pulse generator) device under the skin of your chest, below your collarbone. Wires under the skin connect the IPG to the electrodes in your brain. The IPG sends electrical pulses to the electrodes, regulating signals in that area of the brain.

TMS, a non-invasive technique, applies magnetic fields to stimulate targeted brain areas, offering a neuromodulatory approach that may aid in reshaping neural circuits associated with amblyopia (Figure 6). Both methodologies represent innovative avenues for exploring advanced interventions beyond conventional age limits. ReAlign is also working on implementing Indigenous languages into its app in order to expand its accessibility.

Figure 6: Transcranial Magnetic Stimulation (TMS). TMS is a noninvasive technological breakthrough that involves applying a series of short magnetic pulses to stimulate nerve cells in areas of the brain known to be associated with major depression. The treatment for depression is sometimes called repetitive TMS (rTMS) because of repetitive magnetic pulses are delivered.

CONCLUSION

In conclusion, ReAlign presents a promising solution for the early diagnosis and treatment of amblyopia, showcasing significant advancements in diagnostic efficiency through iterative testing. The app's diagnostic efficiency and accessibility present a valuable resource, especially in regions with limited access to traditional diagnostic tools. The adaptability of ReAlign's neuroplasticity-focused methodology suggests a broader relevance beyond amblyopia, with implications for the management of diverse neurodevelopmental conditions. The adaptability of ReAlign’s approach provides a basis for future research and development, potentially influencing a more comprehensive understanding and management of various neurodevelopmental conditions on a global scale.

REFERENCES

Birch, E. E., Jost, R. M., De La Cruz, A., Kelly, K. R., Beauchamp, C. L., Dao, L., Stager, D., Jr, & Leffler, J. N. (2019). Binocular amblyopia treatment with contrast-rebalanced movies. Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus, 23(3), 160.e1–160.e5.

Holmes, J. M., & Levi, D. M. (2018). Treatment of amblyopia as a function of age. Visual neuroscience, 35, E015. https://doi.org/10.1017/S0952523817000220

Koo, E. B., Gilbert, A. L., & VanderVeen, D. K. (2016). Treatment of amblyopia and amblyopia risk factors based on current evidence. Seminars in Ophthalmology, 32(1), 1–7. https://doi.org/10.1080/08820538.2016.1228408 

Li, Y., Sun, H., Zhu, X., Su, Y., Yu, T., Wu, X., Zhou, X., & Jing, L. (2020). Efficacy of interventions for Amblyopia: A systematic review and network meta-analysis. BMC Ophthalmology, 20(1). https://doi.org/10.1186/s12886-020-01442-9 

Papageorgiou, E., Asproudis, I., Maconachie, G., Tsironi, E. E., & Gottlob, I. (2019). The treatment of amblyopia: current practice and emerging trends. Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 257(6), 1061–1078. https://doi.org/10.1007/s00417-019-04254-w

Povolo, M., Mikesell, R., Sparna, T., & Lonway, S. (2019). Brain development. Leelanau Early Childhood Development Commission. https://www.leelanauearlychildhood.org/brain-development 

Spiegel, D. P., Li, J., Hess, R. F., Byblow, W. D., Deng, D., Yu, M., & Thompson, B. (2013). Transcranial direct current stimulation enhances recovery of stereopsis in adults with amblyopia. Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics, 10(4), 831–839. https://doi.org/10.1007/s13311-013-0200-y

Zagui, R. M. B. (2019, June 25). Amblyopia: Types, diagnosis, treatment, and New     Perspectives. American Academy of Ophthalmology. https://www.aao.org/education/disease-review/amblyopia-types-diagnosis-treatment-new-perspective 

Zheng, J., Zhang, W., Liu, L., & Hung Yap, M. K. (2023). Low frequency repetitive transcranial magnetic stimulation promotes plasticity of the visual cortex in adult amblyopic rats. Frontiers in neuroscience, 17, 1109735. https://doi.org/10.3389/fnins.2023.1109735


ABOUT THE AUTHOR

Ashish Chettimada

Ashish Chettimada is a grade 10 student from Waterloo, Ontario, who is studying in the International Baccalaureate program at Cameron Heights Collegiate Institute. He is passionate about human biology, technology, and the intersection of these fields. Driven by his interests, he has explored different healthcare technology solutions, notably through the development of his diagnostic and management app, ReAlign. Participating in science fairs for many years, Ashish has had the opportunity to showcase his work at national fairs and has had the honour to represent Team Canada at MILSET International 2023, hosted in Puebla, Mexico.