Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review

Advances in Ophthalmology Practice and Research, Journal Year: 2024, Volume and Issue: 4(3), P. 120 - 127

Published: March 25, 2024

The convergence of smartphone technology and artificial intelligence (AI) has revolutionized the landscape ophthalmic care, offering unprecedented opportunities for diagnosis, monitoring, management ocular conditions. Nevertheless, there is a lack systematic studies on discussing integration AI in this field. This review includes 52 studies, explores smartphones ophthalmology, delineating its collective impact screening methodologies, disease detection, telemedicine initiatives, patient management. findings from curated indicate promising performance smartphone-based various diseases which encompass major retinal diseases, glaucoma, cataract, visual impairment children surface diseases. Moreover, utilization imaging modalities, coupled with algorithms, able to provide timely, efficient cost-effective pathologies. modality can also facilitate self-monitoring, remote monitoring enhancing accessibility eye care services, particularly underserved regions. Challenges involving data privacy, algorithm validation, regulatory frameworks issues trust are still need be addressed. Furthermore, evaluation real-world implementation imperative as well, prospective currently lacking. Smartphone merged enables earlier, precise diagnoses, personalized treatments, enhanced service care. Collaboration crucial navigate ethical security challenges while responsibly leveraging these innovations, potential revolution access global health equity.

Language: Английский

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The Hearing Test App for Android Devices: Distinctive Features of Pure-Tone Audiometry Performed on Mobile Devices

Medical Devices Evidence and Research, Journal Year: 2024, Volume and Issue: Volume 17, P. 151 - 163

Published: April 1, 2024

Abstract: The popularity of mobile devices, combined with advances in electronic design and internet technology, has enabled home-based hearing tests recent years. purpose this article is to highlight the distinctive aspects pure-tone audiometry performed on a device by means Hearing Test app for Android devices. first version was released decade ago, since then been systematically improved, which required addressing many issues common majority apps testing. discusses techniques calibration, outlines testing procedure how it differs from traditional audiometry, explores potential bone conduction testing, provides considerations interpreting including test duration background noise. concludes detailing clinically relevant requiring special attention during interpretation results are substantial value hundreds thousands active users worldwide, as well other apps. Keywords: self-testing, threshold,

Language: Английский

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Web- and app-based tools for remote hearing assessment: a scoping review

International Journal of Audiology, Journal Year: 2022, Volume and Issue: 62(8), P. 699 - 712

Published: June 9, 2022

Objective Remote hearing screening and assessment may improve access to, uptake of, care. This review, the most comprehensive to date, aimed (i) identify assess functionality of remote tools on smartphones online platforms, (ii) determine if assessed were also evaluated in peer-reviewed publications (iii) report accuracy existing validation data.Design Protocol was registered INPLASY reported according PRISMA-Extension for Scoping Reviews.Study sample In total, 187 (using tones, speech, self-report or a combination) 101 studies met inclusion criteria. Quality, functionality, bias applicability each app by at least two authors.Results Assessed showed considerable variability functionality. Twenty-two (12%) 14 had acceptable The results their quality varied greatly, largely depending category tool.Conclusion reliability are unknown. Tone-producing provide approximate thresholds but have calibration background noise issues. Speech less affected these issues mostly do not an estimated pure tone audiogram. Predicting audiograms using filtered language-independent materials could be universal solution.

Language: Английский

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Advancing Equitable Hearing Care: Innovations in Technology and Service Delivery

Folia Phoniatrica et Logopaedica, Journal Year: 2023, Volume and Issue: 75(4), P. 201 - 207

Published: Jan. 1, 2023

Hearing loss is a neglected global health priority affecting 1.5 billion persons. Global access to hearing care severely limited with management options, like aids, inaccessible most. The cost and centralised nature of traditional service-delivery approaches in have undermined equitable alongside poor awareness.Recent innovations digital mHealth technologies used by workers through task shifting are enabling novel community-based services across the continuum care. This narrative review explores technology-enabled communities. We provide examples focused on our work over past decade explore more primary, secondary, tertiary levels prevention.Hearing potential increase care, improve quality life for those affected loss, reduce costs associated untreated loss. More that requires scalable models enabled innovative within communities integrated into public initiatives including promotion.

Language: Английский

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Artificial intelligence approaches for tinnitus diagnosis: leveraging high-frequency audiometry data for enhanced clinical predictions

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 7

Published: May 7, 2024

This research investigates the application of machine learning to improve diagnosis tinnitus using high-frequency audiometry data. A Logistic Regression (LR) model was developed alongside an Artificial Neural Network (ANN) and various baseline classifiers identify most effective approach for classifying presence. The methodology encompassed data preprocessing, feature extraction focused on point detection, rigorous evaluation through performance metrics including accuracy, Area Under ROC Curve (AUC), precision, recall, F1 scores. main findings reveal that LR model, supported by ANN, significantly outperformed other models, achieving accuracy 94.06%, AUC 97.06%, high precision recall These results demonstrate efficacy ANN in accurately diagnosing tinnitus, surpassing traditional diagnostic methods rely subjective assessments. implications this are substantial clinical audiology, suggesting learning, particularly advanced models like ANNs, can provide a more objective quantifiable tool diagnosis, especially when utilizing not typically assessed standard hearing tests. study underscores potential facilitate earlier accurate which could lead improved patient outcomes. Future work should aim expand dataset diversity, explore broader range algorithms, conduct trials validate models' practical utility. highlights transformative paving way advancements treatment tinnitus.

Language: Английский

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Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions

Sensors, Journal Year: 2024, Volume and Issue: 24(22), P. 7126 - 7126

Published: Nov. 6, 2024

The integration of artificial intelligence (AI) into medical disciplines is rapidly transforming healthcare delivery, with audiology being no exception. By synthesizing the existing literature, this review seeks to inform clinicians, researchers, and policymakers about potential challenges integrating AI audiological practice. PubMed, Cochrane, Google Scholar databases were searched for articles published in English from 1990 2024 following query: "(audiology) AND ("artificial intelligence" OR "machine learning" "deep learning")". PRISMA extension scoping reviews (PRISMA-ScR) was followed. database research yielded 1359 results, selection process led inclusion 104 manuscripts. has evolved significantly over succeeding decades, 87.5% manuscripts last 4 years. Most types consistently used specific purposes, such as logistic regression other statistical machine learning tools (e.g., support vector machine, multilayer perceptron, random forest, deep belief network, decision tree, k-nearest neighbor, or LASSO) automated audiometry clinical predictions; convolutional neural networks radiological image analysis; large language models automatic generation diagnostic reports. Despite advances technologies, different ethical professional are still present, underscoring need larger, more diverse data collection bioethics studies field audiology.

Language: Английский

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Audiological profile of first-time hearing aid users – implications for the development of a fast-track fitting protocol

International Journal of Audiology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 10

Published: March 3, 2025

Objective To assess the feasibility of implementing a fast-track process (single-session assess-and-fit appointment with no ENT specialist's examination) in hearing rehabilitation by investigating accuracy protocol assignment applying various cut-off criteria and describing audiometric profile patients being evaluated for their first aids.

Language: Английский

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Hearing loss configurations in low- and middle-income countries

International Journal of Audiology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 8

Published: March 6, 2025

The majority of individuals with hearing loss worldwide reside in low- and middle-income countries (LMICs), but there is limited information regarding the characteristics these regions. This descriptive study aims to address this knowledge gap by analysing audiogram patterns LMIC populations. Greater about properties LMICs allows for improved planning interventions. Retrospective data from 23 collaborating centres across 16 were collected. All participants adults seeking help problems. A machine learning approach was utilised classify threshold identify representative profiles. comprised 5773 participants. results revealed mildly sloping audiometric varying severity. differed previous studies conducted high-income regions which included more steeply losses. findings also indicated a higher proportion severe levels loss. These variations could be attributed population-level differences causative mechanisms LMICs, such as prevalence infectious disease-related may reflect health behaviours. highlights need tailored, scalable, interventions LMICs.

Language: Английский

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Applying artificial intelligence to rare diseases: a literature review highlighting lessons from Fabry disease

Orphanet Journal of Rare Diseases, Journal Year: 2025, Volume and Issue: 20(1)

Published: April 17, 2025

Abstract Background Use of artificial intelligence (AI) in rare diseases has grown rapidly recent years. In this review we have outlined the most common machine-learning and deep-learning methods currently being used to classify analyse large amounts data, such as standardized images or specific text electronic health records. To illustrate how these been adapted developed for use with diseases, focused on Fabry disease, an X-linked genetic disorder caused by lysosomal α-galactosidase. A deficiency that can result multiple organ damage. Methods We searched PubMed articles focusing AI, disease published anytime up 08 January 2025. Further searches, limited between 01 2021 31 December 2023, were also performed using double combinations keywords related AI each affected diseases. Results total, 20 included. field, may be applied prospectively populations identify patients, retrospectively data sets diagnose a previously overlooked disease. Different facilitate diagnosis, help monitor progression organs, potentially contribute personalized therapy development. The implementation general healthcare medical imaging centres raise awareness prompt practitioners consider conditions earlier diagnostic pathway, while chatbots telemedicine accelerate patient referral experts. technologies generate ethical risks, prompting new regulatory frameworks aimed at addressing issues established Europe United States. Conclusion AI-based will lead substantial improvements diagnosis management need human guarantee is key issue pursuing innovation ensuring involvement remains centre care during technological revolution.

Language: Английский

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Performance and reliability evaluation of an improved machine learning‐based pure‐tone audiometry with automated masking

World Journal of Otorhinolaryngology - Head and Neck Surgery, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 12, 2024

Abstract Objective Automated air‐conduction pure‐tone audiograms through Bayesian estimation and machine learning (ML) classification have recently been proposed in the literature. Although such ML‐based audiometry approaches represent a significant addition to field, they remain unsuited for daily clinical settings, particular listeners with asymmetric or conductive hearing loss, severe cochlear dead zones. The goal here is expand on previously ML assess performance of this improved large sample wide range status. Methods First, we describe changes made method of: (1) safety limits test status, (2) transient responses cater zones nonmeasurable thresholds, importantly, (3) automated contralateral masking loss. Next, compared conventional manual cohort ( n = 109 subjects) both normal‐hearing hearing‐impaired listeners. Results Our results showed that all audiometric frequencies tested, no difference was found between thresholds obtained using audiometer as methods. Furthermore, test–retest not each frequency tested. Finally, when examining cross‐clinic reliability measures, differences were most Conclusions Together, our validate use adult tests audiometry.

Language: Английский

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