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Venkatayogi, N.; Gupta, M.; Gupta, A.; Nallaparaju, S.; Cheemalamarri, N.; Gilari, K.; Pathak, S.; Vishwanath, K.; Soney, C.; Bhattacharya, T.; et al. Artificial Intelligence-Enhanced Point-of-Care Ultrasound in Low-Resource Settings. Encyclopedia. Available online: https://encyclopedia.pub/entry/47625 (accessed on 21 December 2024).
Venkatayogi N, Gupta M, Gupta A, Nallaparaju S, Cheemalamarri N, Gilari K, et al. Artificial Intelligence-Enhanced Point-of-Care Ultrasound in Low-Resource Settings. Encyclopedia. Available at: https://encyclopedia.pub/entry/47625. Accessed December 21, 2024.
Venkatayogi, Nethra, Maanas Gupta, Alaukik Gupta, Shreya Nallaparaju, Nithya Cheemalamarri, Krithika Gilari, Shireen Pathak, Krithik Vishwanath, Carel Soney, Tanisha Bhattacharya, et al. "Artificial Intelligence-Enhanced Point-of-Care Ultrasound in Low-Resource Settings" Encyclopedia, https://encyclopedia.pub/entry/47625 (accessed December 21, 2024).
Venkatayogi, N., Gupta, M., Gupta, A., Nallaparaju, S., Cheemalamarri, N., Gilari, K., Pathak, S., Vishwanath, K., Soney, C., Bhattacharya, T., Maleki, N., Purkayastha, S., & Gichoya, J.W. (2023, August 03). Artificial Intelligence-Enhanced Point-of-Care Ultrasound in Low-Resource Settings. In Encyclopedia. https://encyclopedia.pub/entry/47625
Venkatayogi, Nethra, et al. "Artificial Intelligence-Enhanced Point-of-Care Ultrasound in Low-Resource Settings." Encyclopedia. Web. 03 August, 2023.
Artificial Intelligence-Enhanced Point-of-Care Ultrasound in Low-Resource Settings
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This entry is adapted from the peer-reviewed paper 10.3390/app13148427

The utilization of ultrasound imaging for early visualization has been imperative in disease detection, especially in the first responder setting. Rapid advancements in the underlying technology of ultrasound have allowed for the development of portable point-of-care ultrasounds (POCUS) with handheld devices. The application of POCUS is versatile, as seen by its use in pulmonary, cardiovascular, and neonatal imaging, among many others. However, despite these advances, there is an inherent inability of translating POCUS devices to low-resource settings (LRS). To bridge these gaps, the implementation of artificial intelligence offers an interesting opportunity. This entry reviews recent applications of POCUS devices within LRS from 2016 to 2023, identifying the most commonly utilized clinical applications and areas where further innovation is needed. Furthermore, the researchers pinpoint areas of POCUS technologies that can be improved using state-of-art artificial intelligence technologies, thus enabling the widespread adoption of POCUS devices in low-resource settings.

point-of-care ultrasound low-resource settings artificial intelligence diagnostic imaging rural health

1. Introduction

Since the 1950s, ultrasounds have been utilized for various medical applications, continuing to be an invaluable diagnostic resource [1]. The utility and penetration of ultrasound has been increasing, supported by an expanding market with an average annual growth rate of 5.9% [2]. This is further supported by the fact that the global medical ultrasound market is expected to reach USD 8.4 billion in 2023 from USD 6.3 billion in 2018 [2]. While ultrasound equipment is predominantly used in obstetrics, it plays a crucial role in other systems such as the heart, eyes, abdomen, and brain [3]. However, due to the complex and delicate nature of ultrasonography machinery, it must be operated by licensed healthcare professionals or individuals who have undergone extensive training [4]. Given high technology costs and reliable infrastructure (electricity and storage) necessities, the inaccessibility of ultrasound technology stretches further into low-resource settings. Therefore, technological advancements in ultrasound are crucial to optimize its use in low-resource settings [5]. In response, the medical device industry has pivoted its focus over the past decade towards creating ultrasounds that are more accessible to all medical professionals, regardless of certification.
One such innovation has been the development of point-of-care ultrasounds (POCUS), which are more affordable, portable, and have demonstrated similar accuracy when compared with cart-based ultrasound for many clinical applications [6]. As newer versions of POCUS continue to develop, the ability to make complex diagnoses has also improved [7][8]. For example, in emergency medicine, where capturing site-specific imaging is crucial, POCUS is applied to support rapid diagnosis [9]. Moreover, POCUS training is now integrated early in medical education with POCUS replacing the stethoscope for several medical schools in the United States [10]. Outside formal education, several qualitative-based protocols for ultrasound assessments, such as Rapid Ultrasound for Shock and Hypertension (RUSH), Focused Assessment with Sonography for Trauma (FAST), and Abdominal and cardiac evaluation with sonography in shock (ACES) have been incorporated into treatment guidelines [11].
Current knowledge about POCUS has been accumulated from developed countries in resource-rich contexts like academic and tertiary hospitals [12][13][14][15][16][17]. While this is a good starting point, there is a critical need to understand the use of POCUS in rural settings and low-resource settings. Low-resource settings (LRS) are defined as “geographical areas where populations have limited access to qualified healthcare providers and quality healthcare services” [18]. LRS represent populations who can benefit from POCUS innovations since there exists a lack of imaging resources, with almost 50% of patients in rural areas requiring transfer to advanced centers for basic imaging [19]. While POCUS shows promise in LRS, barriers such as limited training, maintenance, and on-site analysis remain challenges [20]. Therefore, POCUS research has been focused on implementing and evaluating training systems to increase the usage of POCUS in practice in LRS [21]. According to Sepulveda-Ortiz et al., while POCUS has been utilized in short-term medical missions, training is necessary to ensure that local providers are able to utilize ultrasound machines [22]. Training with POCUS would improve the continuity of care to local patients and bridge the aforementioned gaps in access to imaging technology [22].
However, training systems present their own set of challenges. The reality is that ultrasound training is costly, time-intensive, difficult to scale up, and challenging to standardize across clinics, especially given the wide variety of applications and user groups involved [23][24]. To address this issue, artificial intelligence (AI) provides a unique potential to fill the gap. AI has been previously employed in telemedicine applications such as remote image guidance, in training methods for operators with minimal traditional ultrasound training, and in the analysis of the vast amount of data generated by ultrasound imaging [25].
Several reviews have focused on the integration of general ultrasound with AI to expand its applications and utilization [26]. Additionally, these reviews mentioned the benefits of AI in LRS where AI can transform POCUS into a continuous monitoring device [27]. Despite the creation of such algorithms as well as their widespread usage and integration with ultrasound technology, minimal progress has been made in applying them to POCUS due to limitations of increased noise, blur, and distortions found in POCUS images, as well as difficulties in saving images to build a database [27]. Self et al. introduced the CALPOCUS project as a protocol to collect data and develop machine learning (ML)-based decision-making tools even for non-expert users [25]. Existing reviews such as the ones performed by Doig et al., Buonsenso et al., and Baloescu et al. do not provide us with a comprehensive understanding of the state of AI-enhanced POCUS in LRS due to the limited scope of the research [28][29] and limited retrieval of the literature [30]. This entry aims to conduct a thorough research of the current state of POCUS-based imaging in LRS, examine the utilization of AI-enhanced POCUS in such settings, and explore the future directions necessary to elevate the standard of care in these areas.

2. Clinical Applications of POCUS in LRS

The organ systems where POCUS was most utilized were the lungs or in preparation for general prenatal care [23]. For antenatal care in LRS, POCUS devices have been utilized to detect unique or crucial fetal markers such as heartbeats or the fetal head [31][32]. Studies have also been able to highlight the utilization of POCUS in LRS settings to identify pneumonia or diagnose cardiorespiratory symptoms [33][34][35]. For cardiac applications, POCUS devices have been used to evaluate aortic stenosis with anticipation of expanded utility to assess stenosis of other cardiac valves [36]. Direct training using a POCUS device allowed for increased diagnostic accuracy from younger medical students in comparison to traditional cardiac auscultation [37]. Additional case studies have also demonstrated the utility of POCUS devices not only for the cardiac system but also in the vasculature, whether it be for peripheral artery disease or aneurysms [38][39]. 
In the pulmonary space, POCUS devices have the potential to help elucidate various forms of pleural effusions by evaluating various features such as anechoic fluid, septations, fibrin strands, and the sonographic appearance of the pleural fluid [40][41].
In developed countries, these devices are heavily employed in general practice or even trauma settings to make instantaneous point-of-care decisions crucial to a patient’s life [42][43]. There was only a singular similar study in LRS that reported a 3% negative impact due to the use of POCUS imaging [44]. Nixon et al. specified that POCUS should not be used when other imaging modalities are indicated based on disease presentation; rather it should be used as an additional complimentary technique [44]. Regardless, each of these studies highly suggest the integration of such techniques into the trauma found in LRS could expedite treatment and improve patient care.
Due to its sheer value within acute conditions, POCUS devices show extreme promise in evaluating the retroperitoneal area, where both the pancreas and appendix are found, as well as bowel obstructions [45]. Although not specific to LRS, a review of various primary studies has shown extremely high sensitivity and specificity (>90%) for early appendicitis diagnosis using POCUS [45]. Similarly, POCUS devices have had nearly 100% diagnostic accuracy for evaluating pancreatic pathologies with great promise in early diagnosis of bowel obstructions with sensitivity ranges above 95% and high specificity as well [45][46]. Specifically, POCUS devices have been shown to promptly exact key discoveries such as bowel dilation, altered peristalsis, fluid accumulation, as well as wall thickening and collapsed colonic segments [47].
Another area of POCUS application is to evaluate renal pathology, specifically diagnosis of masses such as cysts and solid tumors. The researchers did not find clearly defined POCUS applications for renal pathology in LRS. Similarly, liver fibrosis is another disease that is largely diagnosed through ultrasonography due to difficulties in retrieving a liver biopsy due to nearby vasculature [48]. POCUS devices are yet another form of ultrasonography that can help differentiate the difference in matrix stiffness between different areas of the liver [48]. Specifically, POCUS would allow for the identification of characteristics such as caudate lobe enlargement or additional streaks surrounding the hepatic portal branches [49].
On a related note, POCUS devices hold exceptional promise in the neurological realm for examining the optic nerve. They serve as a unique non-invasive method that not only detects damage to the optic nerve but also looks at the longstanding effects of increased intraocular pressure such as optic disc swelling [50]. Furthermore, both intraocular pressure and optic disc swelling are highly correlated with intracranial pressure, a crucial metric in managing neurological pathologies [51]. While these applications were not focused in LRS, these examples demonstrate the potential of POCUS to serve as a novel method for point-of-care and a non-invasive method for the detecting various pathologies, thus revolutionizing the state of bedside medicine in LRS.

3. Implementation of POCUS in LRS and Barriers

These applications in LRS may face various barriers that hinder the implementation of POCUS, particularly due to limited training and accurate on-site analysis and data extrapolation [29]. Limited training is an ongoing challenge that is actively being explored through training exercises and regiments or telemedicine. A previous study by Vinayak et al. was able to train midwives to utilize POCUS devices alongside mobile phones and tablets to capture high-enough resolution images to identify crucial features within images [52]. Similarly, Kwon et al. found that the majority of eighth graders were able to obtain adequate POCUS (FAST) images, even with minimal training, emphasizing the effectiveness of training and the ease of use of POCUS as an imaging modality [53]. Although the ability to teach the layperson is extremely helpful, these past methodologies rely on the continuous assurance that everyday citizens remember this training. An alternative for such a roadblock is the utilization of telemedicine. Despite limited studies within the realm of LRS POCUS, Wang et al. offered a unique setup revolving around augmented reality telemedicine to allow for the continuous ability for real-time training [54]. Regardless, there has been little research into the further development of ultrasound telemedicine applications [55]. Further research should be encouraged to transition these past studies towards POCUS modalities that can be implemented in low-resource settings.

4. Potentials and the Integration of AI-Enhanced POCUS in LRS

Due to the limited training and medical certification available in low-resource settings, diagnosing conditions using POCUS becomes difficult [56]. AI can play a crucial role in improving diagnoses through the integration of machine learning modalities with general ultrasound for pathology detection, feature extraction, and disease diagnosis [54][57]. For example, studies by Prabusankarlal et al. and Singh et al. utilized support vector machine (SVM) and artificial neural network (ANN) learning models, respectively, to diagnose breast masses with around 96% accuracy [58][59]. Similarly, the employment of ANNs and SVM models can help directly diagnose cirrhosis and fatty liver disease, amongst others, in the liver as well as lesion margin in the thyroid with extremely high accuracy [60][61][62]. Additionally, machine learning has the ability to augment current diagnostic procedures within ultrasound technology for commonly seen pathologies such as those found in the heart or lungs [62][63][64].
Despite these advances, there is a significant lack of studies focusing on AI applications in POCUS. While there are advancements in AI-enhanced POCUS and some studies exploring its use in LRS, none of these studies met the inclusion criteria for this research. Nonetheless, there are notable examples of POCUS and AI integration, particularly in the areas of COVID-19 and obstetrics. For instance, Cheema et al. developed a deep learning model that assisted sonographers in obtaining high-quality cardiac ultrasound images in the COVID-19 intensive care unit by utilizing a model trained on the hand movements of skilled cardiac sonographers [65]. Cheema et al. describe a ML-based algorithm that was used to estimate fetal gestational age estimation through POCUS [65]. Similarly, Pokaprakarn et al. developed an AI model that estimated gestational age with an accuracy that performed similarly to trained sonographers using low-cost ultrasound technology with data obtained from volunteers in Zambia and North Carolina [66]. Additionally, Kuroda et al. compared AI-POCUS with CT scans, and they found that AI-POCUS achieved a 94.5% accuracy in detecting CT-validated pneumonia [67]. Blaivas et al. analyzed six common DL image classification algorithms, and found that older and less complex CNN performed the best [68].
One major challenge in developing AI algorithms for POCUS lies in the limited availability of publicly available datasets, and the diversity of POCUS applications and ultrasound equipment makes it hard to standardize datasets [24]. To address this limitation, recent work has employed techniques such as CycleGAN to generate synthetic POCUS images, enabling the classification of breast cancer with a 95% confidence interval for AUC between 93.5 and 96.6 [69].
As AI integration continues to expand in healthcare, the researchers anticipate that these models will likely be adapted for POCUS devices in preparation for their implementation in LRS. Through this research, it is evident that POCUS holds significant advantages for healthcare delivery in various LRS across multiple clinical specialties. The integration of AI in POCUS presents a promising opportunity to overcome existing barriers and advance the applications within LRS.
Furthermore, AI-enhanced POCUS plays a crucial role in supporting the growth of telemedicine, a field that has been more increasingly adopted after the disruption of in-person hospital visits during the COVID-19 pandemic. It enables skilled clinicians to provide remote guidance and interpretation of ultrasound images, consequently facilitating remote monitoring and reducing the need for costly specialized visits. Specifically, the use of AI to help improve image quality, regardless of the user’s training level, yields diagnostically relevant images [70]. Additionally, by harnessing the power of augmented reality in combination with artificial intelligence, a remote teleguidance system can be designed to assist novice users with probe placement as well as image interpretation. Furthermore, AI can be used for direct quantification of crucial clinical metrics essential for diagnosis [71]. For instance, AI-enhanced POCUS can be used in cardiac applications for left ventricular ejection fraction, vena cava measurements, or blood velocity time integrals [71]. The system’s real-time image interpretation and analysis is immensely valuable for remote consultations and monitoring. Ultimately, AI-enhanced POCUS empowers healthcare providers in LRS to embrace telemedicine, allowing them to overcome training and geographical barriers and improve access to specialized care.

5. Future Perspectives and Research Opportunities

In the future, several strategies can be implemented to leverage AI and advance the state of POCUS in LRS. One approach is to develop training programs that provide personalized feedback to clinicians in LRS that can enhance their proficiency in performing POCUS examinations. Another important focus should be on the implementation of POCUS in local clinics, expanding access to this technology to a broader population. The adoption of implementation protocols similar to CALPOCUS can enhance ultrasound image quality and maximize diagnostic information in scans by providing automated image acquisition and real-time feedback on image quality and probe positioning. Such enhancements would contribute to the development of robust AI models capable of real-time analysis of POCUS data. Since it is not always feasible to collect large amounts of POCUS data across various clinical specialties, using complex deep learning algorithms to generate synthetic POCUS data as described in Karlsson et al. and understanding the differences between general ultrasound and POCUS images to perform image-to-image translations, as introduced in Jafari et al., shows great promise [72].
Although research specifically addressing the combined application of POCUS and AI in LRS remains limited, there is significant potential for growth in this field, and further research is warranted. Considering the existing evidence of POCUS and its associated benefits, it is evident that AI-enhanced POCUS has the potential to revolutionize healthcare delivery, bridge the healthcare disparity gap, and ensure quality care for underserved populations.

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Nethra Venkatayogi
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Maanas Gupta
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Alaukik Gupta
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Shreya Nallaparaju
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Nithya Cheemalamarri
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Krithika Gilari
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Shireen Pathak
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Krithik Vishwanath
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Carel Soney
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Tanisha Bhattacharya
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Nirvana Maleki
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Saptarshi Purkayastha
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Judy Wawira Gichoya
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