features of the eye and orbit. However, the interpretation of the data obtained from such studies is best left to

expert physicians and technicians who are trained and well-versed in analyzing such images. This technology

can provide high-resolution information regarding anatomic and functional changes. In recent years, imaging

techniques have developed rapidly, along with therapeutic advances. However, with the increasing

sophistication of imaging technology, comprehension and management of eye disease has become more

complex due to the large numbers of images and findings that can be recorded for individual patients, as well

as the hypotheses supported by these data. Thus, each patient has become a “big data” challenge. Conventional

high rate of misdiagnosis and wastage of medical data. The new era of clinical diagnostics and therapeutics

urgently requires intelligent tools to manage medical data safely and efficiently.

are designed for point-of-care use. Since most healthcare workers already possess smartphones,

combining the two could be cost-effective compared to traditional ophthalmic imaging equipment. This

approach also saves the patient and their relatives time and money travelling to areas with conventional

imaging equipment.

dedicated power source, which is a major advantage in areas with limited infrastructure. Given that

mobile phones are ubiquitous, even in remote areas, this technology is readily accessible.

training for healthcare workers to operate. Mobile phone applications can further simplify the process

provide valuable information for diagnosing conditions such as cataracts, retinal detachments, retained

intraocular foreign bodies, and ocular trauma. For certain conditions, such as assessing optic nerve

head cupping in glaucoma, ultrasound may even be preferred over other imaging modalities.

performed by individuals with varying levels of training. Additionally, internet connectivity may be

unreliable in remote areas, limiting the ability to transmit images for remote consultation. Power

outages, experienced frequently in some developing countries like India and Nigeria, are also a

challenge.

sonography will improve overall healthcare delivery and create awareness of the alternative, compared

mobile phones holds promise for expediting healthcare delivery and improving access to ophthalmic care

(especially in developing countries where ultrasound services may not be readily available). Collaboration

between healthcare providers, technology developers, and policymakers will be essential to maximize the

potential of this approach.

In many regions across the globe, access to specialized healthcare services remains a challenge, particularly in

remote areas. This limitation is especially pronounced in ophthalmic care, where timely diagnosis and

management are crucial for preventing irreversible vision loss. However, amidst these challenges,

technological innovations offer a beacon of hope in the form of portable ultrasound devices coupled with

ubiquitous mobile phones. By harnessing the power of these technologies, healthcare delivery can be

expedited, bridging the gap between patients and essential ophthalmic services. Portable ultrasound devices

In the Indian rural context, for example, the integration of portable ultrasound devices with mobile phones has

particular relevance for preventing avoidable blindness. Large segments of the population in remote villages

present late with advanced ocular disease, often due to lack of nearby specialist services, long travel distances,

and the financial burden associated with seeking care at tertiary centers. Many of these conditions, including

advanced cataract, vitreous hemorrhage, retinal detachment, or occult ocular trauma, can lead to irreversible

visual impairment if not detected and treated promptly. A point-of-care ophthalmic ultrasound device,

deployed at primary health centers, vision centers, or during community outreach camps, offers a practical

means to identify sight-threatening pathology early and to intervene before blindness becomes permanent. This

is particularly important in India, where the social and economic consequences of preventable blindness extend

beyond the individual to entire families who depend on a single wage earner.

The emotional impact of preventable blindness in rural communities underscores the urgency of timely

diagnosis. Patients who lose vision in one or both eyes due to conditions that could have been detected earlier

frequently experience profound psychological distress, loss of independence, and reduced quality of life. Point-

ophthalmic conditions, which often require specialized imaging modalities such as optical coherence

tomography (OCT) or ultrasound biomicroscope (UBM). However, these modalities are typically confined to

tertiary healthcare facilities, leaving many patients in underserved regions without access to essential

diagnostic services. By integrating portable ultrasound devices with mobile phones, healthcare providers can

bring diagnostic capabilities directly to the point of care, whether it be a rural clinic, a community health

center, or even a patient's home. This paradigm shift not only reduces the burden on centralized healthcare

facilities but also empowers frontline healthcare workers to make informed clinical decisions in real-time.

Furthermore, the synergy between portable ultrasound devices and mobile phones facilitates remote

consultation and collaboration with ophthalmic specialists. Through telemedicine platforms, images captured

on a mobile phone can be securely transmitted to experts for interpretation, enabling timely diagnosis and

Figure 2. Retinoblastoma

There are different ways of performing ultrasound-based diagnostic procedures. Depending on the application,

the sonographer acquires either a single image or an image series. The second approach is better when a further

automated image processing step is introduced. Simultaneous analysis of multiple data sets provides reliable

results, less prone to artifacts and outliers. At the same time, the analysis of the entire recording might be

disturbed by strongly distorted data or artifacts influencing the geometry of visualized structures, appearing in

part of the frames. Consequently, it leads to misclassification, false-positive detections, and finally, inaccurate

results of measurements. Therefore, the overall goal of this study was to develop and evaluate the classification

framework, which enables robust and fast POCUS series analysis.

is a superficial structure filled with fluid. Over the last two decades, several scientific publications have

documented that ocular ultrasound in emergent or critical care settings is an accurate diagnostic tool and

expands and improves emergency diagnosis and management.

There is an abundance of ultrasound datasets for various use-cases, which can be used to generate DNN-based

models for classification and segmentation. For instance, the breast ultrasound image dataset presented by Al-

Dhabyani et al. (Al-Dhabyani et al., 2020), which is composed of normal, benign, and malignant images that

can be used to train a model to act as a classifier. Similarly, the POCUS dataset, presented by Born et al. (Born

et al., 2020), and the COVIDX-US dataset, by Ebadi et al. (Ebadi et al., 2021), are openly accessible for

building DNN-based clinical assistants that can aid in the analytics and diagnosis of COVID-19. Leclerc et al.

(Leclerc et al., 2019) presented a cardiac ultrasound electrocardiography dataset containing image sequences

contributions that utilize ML advances to solve current challenges in medical USD, (2) to discuss future

opportunities that will utilize ML techniques to further improve clinical workflow and USD-based disease

diagnosis and characterization.

particularly for ophthalmic conditions, presents significant challenges. Timely diagnosis of ophthalmic

inaccessible in these settings. However, integrating artificial intelligence (AI) with ultrasound technology

offers a promising solution to enhance early detection and diagnosis. This paper explores the potential of AI-

assisted ultrasound in improving the detection of ophthalmic disorders in austere environments, thereby

facilitating timely interventions and mitigating the burden of vision impairment.

The integration of AI-assisted ultrasound technology holds tremendous promise for improving the early

diagnosis of ophthalmic disorders in austere environments. By enhancing the diagnostic capabilities of

frontline healthcare providers and facilitating remote collaboration with specialists, AI-assisted ultrasound has

the potential to revolutionize ophthalmic care delivery, ultimately reducing the burden of vision impairment

and improving patient outcomes in challenging settings.