SDG3 Health App: Advancing Global Well-being

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konma_org
Project Owner

SDG3 Health App: Advancing Global Well-being

Funding Requested

$21,000 USD

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Overview

The SDG3 health app revolutionizes anemia screening with AI, offering non-invasive, real-time hemoglobin assessments using images of the lower palpebral conjunctiva. This app supports SDG3's goals by facilitating quick, accurate anemia detection through a deep neural network trained on 25,000+ data points. Designed to eliminate invasive procedures, it reduces patient discomfort and biohazard waste, making it ideal for mass screenings in diverse and underserved areas. Its ability to deliver immediate results enhances healthcare efficiency by enabling swift medical decisions.

Proposal Description

How Our Project Will Contribute To The Growth Of The Decentralized AI Platform

Our SDG3 health app for anemia screening demonstrates the practical use of AI in healthcare by applying a deep neural network trained on extensive data for non-invasive diagnostics. This project will attract users and developers, enhancing the AI platform's reputation and encouraging further innovation. By improving AI algorithms and expanding the platform's capabilities, our app fosters a vibrant ecosystem, driving growth and collaboration in AI-driven healthcare solutions.

Our Team

Our team comprises seasoned professionals with expertise in AI, healthcare, and project management. This includes medical doctors, AI engineers, and strategic planners who have successfully implemented technology-driven health projects globally. Our diverse skills and proven track record ensure we are uniquely equipped to develop, deploy, and scale this innovative anemia screening app, making a substantial impact on global health.

View Team

AI services (New or Existing)

Deep Learning Algorithm

Type

New AI service

Purpose

Utilizing advanced neural networks trained on large datasets to accurately analyze images of the lower palpebral conjunctiva and estimate hemoglobin levels.

AI inputs

Images of the lower palpebral conjunctiva captured via smartphone cameras along with user demographic data (age gender etc.).

AI outputs

Estimated hemoglobin levels based on image analysis.

Image Processing Technologies

Type

New AI service

Purpose

Employing sophisticated image recognition and processing techniques to ensure that the images captured through the app are of high quality and suitable for analysis.

AI inputs

Raw images captured by users.

AI outputs

Enhanced and standardized images ready for accurate analysis including adjustments for lighting focus and alignment.

Natural Language Processing (NLP)

Type

New AI service

Purpose

Integrating NLP to provide interactive educational content within the app aiding users in understanding their health data and learning about anemia prevention and treatment.

AI inputs

User queries and interactions within the app; potentially textual input from educational resources.

AI outputs

User-friendly responses and informational content processed and tailored to be easily understandable enhancing the user’s knowledge about anemia.

Predictive Analytics

Type

New AI service

Purpose

Using AI to analyze user data over time to predict trends in anemia across different populations and regions which can inform public health strategies.

AI inputs

Aggregated user data over time including hemoglobin levels geographic data and other health indicators.

AI outputs

Predictions and trends about anemia prevalence and severity in different demographics and regions useful for public health planning and intervention.

Company Name (if applicable)

Ameya Life

The core problem we are aiming to solve

Anemia is a global health challenge affecting over 1.5 billion people, leading to severe public health consequences and economic burdens, particularly in developing regions. Current diagnostic methods for anemia are predominantly invasive, requiring blood samples, which poses significant challenges in terms of accessibility,cost,discomfort and risk of infection.These barriers are amplified in remote and underserved areas where healthcare resources are limited and the prevalence of anemia is notably high.Our project aims to address this critical issue by developing a non-invasive, cost-effective, and highly scalable solution for anemia screening.By leveraging advanced AI technology, our solution will facilitate rapid and accurate hemoglobin level assessments without the need for blood draws, significantly improving the accessibility and efficiency of anemia diagnosis.This approach not only enhances patient comfort and safety but also supports wider public health efforts to combat anemia and improve health outcomes on a global scale.

Our specific solution to this problem

Our solution to combat anemia utilizes a mobile health app powered by AI to perform non-invasive screenings using images of the lower palpebral conjunctiva. The app employs a deep learning algorithm trained on over 25,000 data points, allowing it to analyze eye images to estimate hemoglobin levels accurately. This approach eliminates the need for blood draws, reducing discomfort and infection risk.

The app's design focuses on simplicity and accessibility, making it suitable for widespread use, especially in remote and underserved areas. Users capture an eye image using their smartphone, and the app processes this to provide hemoglobin readings in seconds. This process is not only user-friendly but also significantly lowers the cost and logistical barriers associated with traditional anemia screening methods.

By integrating with health management systems, the app ensures data is immediately available for healthcare providers, enabling prompt decision-making for further diagnostics or treatment. This seamless integration enhances the efficiency of healthcare delivery and expands the reach of anemia screening efforts globally. Our solution thus bridges the gap in anemia diagnosis, offering a scalable, cost-effective, and efficient tool that aligns with global health objectives.

Project details

Our project not only introduces a revolutionary method for anemia screening but also sets the groundwork for broader applications in global health diagnostics through artificial intelligence. Here are several key points that highlight the broader impact and innovative aspects of our project:

1. Community and Healthcare Integration: We aim to seamlessly integrate our application with existing healthcare systems and community health programs. This integration will allow for real-time data sharing and analytics, which can inform public health strategies and interventions at community, regional, and national levels. By connecting with health workers and facilities, we ensure that individuals identified as anemic can receive timely and appropriate care.

2. Educational Component: A crucial part of our project is education. The app will include educational resources about anemia—its causes, effects, and treatment. This feature aims to raise awareness and knowledge, empowering individuals to seek care proactively and manage their health more effectively.

3. Scalability and Adaptability: While initially focused on anemia, the technology developed can be adapted to screen for other conditions that can be diagnosed through visual indicators. This adaptability could significantly expand the app's impact, making it a versatile tool in global health.

4. Data Privacy and Security: We prioritize user privacy and data security. Our application complies with global data protection regulations, ensuring that all user data is encrypted and stored securely. Users have control over their data, with clear consent protocols in place.

5. Sustainability Model: The project is designed with sustainability in mind. Beyond the initial funding phase, we plan to collaborate with government bodies, NGOs, and private sectors to ensure ongoing support and development. This includes exploring revenue models like partnerships with health insurance companies to include our screening as a benefit.

6. Research and Continuous Improvement: We are committed to continuous research and development. By collecting anonymized data from app users, we can refine our algorithms, improve accuracy, and expand our database, which in turn will enhance the app’s functionality and the reliability of its screenings.

7. Impact Measurement: We will implement a robust framework for measuring the health outcomes and economic impact of our app. This includes tracking the reduction in incidence rates of anemia, improvement in patient quality of life, and analysis of cost savings for healthcare systems.

8. Inclusivity and Accessibility: The app is designed to be accessible to as wide a user base as possible, including features for low-literacy and non-English speaking users. Voice-assisted instructions and multi-language support are planned to ensure inclusivity.

By addressing these areas, our project not only tackles anemia but also contributes to the broader goals of improving global health infrastructure, enhancing patient care, and democratizing health information through technology.

Competition and USPs

Our solution stands out by offering a non-invasive, AI-driven method for anemia screening, which eliminates the discomfort and biohazard risks associated with traditional blood tests. Unlike other solutions that require specialized equipment or medical expertise, our app uses simple smartphone camera technology, making it accessible and cost-effective for widespread use, especially in remote areas.

The app’s deep learning algorithm, trained on a robust dataset, ensures high accuracy comparable to laboratory tests. This technology not only simplifies the screening process but also provides instant results, enhancing the efficiency of healthcare delivery.

Market success will be driven by partnerships with healthcare providers, NGOs, and governments, integrating the app into existing health programs. Its scalability and the ability to adapt to other diagnostic needs provide a competitive edge, ensuring long-term viability and impact in the global health market.

Open Source Licensing

LGPL - Lesser General Public License

Revenue Sharing Model

Token Allocation

We are planning for a token launch in:

2024-Q4

If awarded by Deep Funding, we will allocate this percentage of the total token supply to SNET / Deep Funding:

5

Token Description (type, value, utility):

Utility Token: SDG3 Health Token (SDG3-HT)

  • Purpose: Serve as a medium of exchange within the app for services, rewards, and governance.
  • Use Cases:
    • Access to Advanced Features: Users can spend tokens to access premium features, such as detailed health insights, personalized health plans, or consultations with medical professionals.
    • Community Engagement and Rewards: Users earn tokens by contributing to the community, such as sharing educational content, participating in health challenges, or providing peer support. These tokens can be redeemed for health-related products or services.
    • Data Sharing Incentives: Users can opt to share their anonymized health data for research and receive tokens as a reward. This supports medical research and improves the app’s AI capabilities.
    • Governance: Token holders can vote on key decisions about the app’s development and features, fostering a democratic governance model that aligns with SDG goals.

Proposal Video

Placeholder for Spotlight Day Pitch-presentations. Video's will be added by the DF team when available.

  • Total Milestones

    4

  • Total Budget

    $21,000 USD

  • Last Updated

    20 May 2024

Milestone 1 - API Calls & Hostings

Description

This milestone represents the required reservation of 25% of your total requested budget for API calls or hosting costs. Because it is required we have prefilled it for you and it cannot be removed or adapted.

Deliverables

You can use this amount for payment of API calls on our platform. Use it to call other services or use it as a marketing instrument to have other parties try out your service. Alternatively you can use it to pay for hosting and computing costs.

Budget

$5,250 USD

Milestone 2 - Architecture and Development of Segmentation Model

Description

The primary objective is to develop a segmentation model that will auto-crop the conjunctiva region given an eye image. This model should be trained on datasets of conjunctiva images shared by SDG3 Health and uses algorithms to identify the conjunctival region from the whole conjunctiva image that has been captured by the user for identifying the anaemia status in a person. Architecture and development of the segmentation model involve the following steps - 1.Data Annotation: Data annotation is the process of labelling the conjunctiva images to create a dataset that the segmentation model can learn from. Annotate the conjunctiva images shared by SDG3 team. This annotation process involves marking the regions of interest namely the conjunctiva regions in these images. 2.Training the Segmentation Model: Training the segmentation model involves using the annotated dataset to teach the model how to accurately identify and segment the regions of interest (ROIs) within the conjunctiva images. The model learns the patterns features and characteristics of these regions through deep learning algorithms and techniques. Once trained the model can generalize its knowledge to segment new unseen images and identify regions of interest (ROIs) within the conjunctiva images.

Deliverables

To provide a segmentation model employing advanced deep learning technology. This model will proficiently segment the conjunctiva region from the input conjunctival image.

Budget

$7,000 USD

Milestone 3 - Development of an App for Data Collection

Description

The primary objective is to ensure the collection of clear and high-quality data to train the anaemia identification model. This phase involves developing a mobile app for data collection and gathering test data including hemoglobin (HB) levels and patient details such as gender age and pregnancy status along with conjunctiva images using the designated data collection app.

Deliverables

To deliver a comprehensive data collection app that enables users to easily gather anaemia test subject data in a structured manner and store it effectively.

Budget

$5,000 USD

Milestone 4 - Architecture & Development of Anemia ID Model

Description

The trained anaemia identification module will consist of the following components - i.Optimal Frame Selection Algorithm: As highlighted within the Mobile App context the core objective of the optimal frame selection algorithm remains consistent. The optimal frame selection algorithm is a technique used in video processing to choose a subset of frames from the conjunctiva video sequence uploaded by the doctor. This module operates on the server side. Upon the field executive uploading the video the algorithm initiates the optimal frame selection. From a minimum of 3 seconds of video we generate a minimum of 90 frames with each second yielding around 30 frames. The algorithm efficiently selects the most pertinent frames from this pool a minimum of three and a maximum of ten. ii.Segmentation Module: In this phase we employ a more robust iteration of the segmentation model developed during phase one. This heavier segmentation model effectively segments the region of interest (ROI) corresponding to the conjunctiva area from the selected optimal frames. iii.HB Value Predictor Module: After segmentation this module conducts regression analysis on the conjunctiva images predicting the hemoglobin (HB) values and subsequently delivering an average hemoglobin reading.

Deliverables

A final fully functional anaemia identifier model that utilizes ML technology demonstrating an improved accuracy compared to the previously developed model. The new model can identify anaemia or non-anaemia based on user data including conjunctiva image age gender and pregnancy status with a solution accuracy of ~95%. This means the model can accurately identify anaemia in ~95 out of 100 tests.

Budget

$3,750 USD

Join the Discussion (1)

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1 Comment
  • 0
    commentator-avatar
    HenriqC
    May 20, 2024 | 3:20 PM

    Super cool and valuable cost saving use case. Hope to see it on the platform available to all these great health apps. Well written proposal too. I just wish it was shortly explained in the proposal what this technology (measuring hemoglobin from the eye) is based on. I think many don’t know about it and I myself just read about it online. Anyway, the test numbers in the attachment were convincing. Any time estimates for the project?

Reviews & Rating

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12 ratings
  • 0
    user-icon
    Gombilla
    Jun 10, 2024 | 11:48 AM

    Overall

    5

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Potnetial to minimize biohazard waste

    As a community health enthusiast, I am drawn to the fact that this project will enable mass screenings in diverse and underserved areas by eliminating the need for invasive procedures, reducing patient discomfort, and minimizing biohazard waste.

    For also, this project is a demonstration of the transformative potential of AI-driven innovation in healthcare, showcasing its ability to address pressing public health challenges and improve patient outcomes.  Cheers !

  • 0
    user-icon
    Nicolad2008
    Jun 10, 2024 | 4:34 AM

    Overall

    3

    • Feasibility 3
    • Viability 4
    • Desirabilty 3
    • Usefulness 3
    encourage innovation

    With the ability to non-invasively detect anemia through imaging of the sub-eyelid conjunctiva, this application supports the SDG3 goal by providing a quick and accurate diagnosis. Trained on more than 25,000 data points, the app's deep neural network can eliminate invasive procedures, reduce patient discomfort and biohazard waste, and increase efficiency healthcare by allowing quick medical decisions. This application not only attracts users and developers, but also enhances the reputation of the AI ​​platform, encouraging innovation. However, the project also faces some limitations such as dependence on image quality and the need for acceptance from the medical community. Additionally, scaling up applications to serve diverse and underserved areas remains a challenge that needs to be addressed.

  • 0
    user-icon
    Vuthuthuy031096
    Jun 10, 2024 | 3:58 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    SDG3 Health App: Advancing Global Well-Being

    - Proposal to develop an SDG3 mobile application integrating AI technology to revolutionize non-invasive AI screening for anemia using images of the lower eyelid conjunctiva. I appreciate the team's creative idea, of using AI technology in the healthcare field to help enhance user experience to make quick medical decisions.
    - The team uses existing mobile phone camera technology and deep learning algorithms to make analyzing images and making analytical predictions easier and more feasible.
    - In terms of team capacity, I see that the team consists of people with experience and capacity in the fields of AI, health care, and project management, which are the necessary skills to complete the project. This. However, I have not seen the complete information of the group members. The group added additional information about the members to make the project more feasible. I wonder if this project is quite large in scale and only 2 people are implementing the project, whether they have enough capacity and resources to develop the project well.
    - The group came up with a fairly specific and detailed project implementation plan. Project implementation milestones have timelines, task plans, and budget allocations. The team should add a timeline associated with each milestone such as the date and month of implementation and completion of the project in each milestone.
    - Non-invasive approach and use of deep learning algorithms trained on more than 25,000 data points help detect anemia quickly and accurately. I find this to be a useful application in practice that saves costs, and is non-invasive so as not to cause discomfort to the user, on the contrary, the platform provided is user-friendly using existing mobile phone technology. This project helps promote the reputation and credibility of AI technology, promoting growth and innovation in AI applications in healthcare.

  • 0
    user-icon
    BlackCoffee
    Jun 10, 2024 | 12:25 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Did the team encounter any challenges?

    I want the author to look more into reality to present challenges in the process of implementing this proposal. Will the team face any challenges? I think yes because any process towards success must go through challenges. That is the reality that I am correctly looking at reality.

  • 0
    user-icon
    TrucTrixie
    Jun 9, 2024 | 1:39 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Compliments for proposal presentation

    What I like most is the presentation. The milestones are presented quite specifically. It speaks to the team's investment of time and intelligence to produce such a product. It's quite easy to understand and can be understood by beginners (though not completely). I commend the team for their presentation of turning the complex into simple.

  • 0
    user-icon
    Max1524
    Jun 8, 2024 | 9:42 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    A highly rated application

    Anemia has never been a problem globally. The reality is that it is not just a problem for a few countries but has become a common concern for humanity. With the integration of high-tech AI - I hope this problem is overcome. I agree with using an AI-powered mobile health app to step-by-step the problem. Of course it cannot solve 100% of the anemia problem, but it is clear that we have much better results than previous results.

  • 0
    user-icon
    CLEMENT
    May 31, 2024 | 2:53 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    This eliminates invasive screening procedures

    This is quite an innovative approach at Anemia screening, considering that the field on diagnostic medicine is gradually advancing towards an all non-invasive realm. This welcome approach revolutionizes anemia screening by leveraging AI to offer non-invasive, real-time hemoglobin assessments. On a larger scale, this addresses a critical healthcare need, particularly in underserved areas, by reducing patient discomfort, biohazard waste, and the time required for screening. The app's ability to deliver immediate results enhances healthcare efficiency, enabling swift medical decisions and ultimately contributing to advancing global well-being in alignment with SDG3's goals.

    Kudos to the proposing team !

  • 0
    user-icon
    Tarran
    May 29, 2024 | 5:33 AM

    Overall

    4

    • Feasibility 3
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    a revolutionary solution in the SDG3 health app

    AI-powered anemia screening is proposed as a revolutionary solution in the SDG3 health app by Ameya Life. The endeavor to provide non-invasive, real-time hemoglobin testing is praiseworthy and fits in nicely with the objectives of global health. The initiative is creative and has the potential to make a big difference, especially in underprivileged communities. Unfortunately, the proposal is devoid of specific information about the team's qualifications, which is essential for determining the project's feasibility. It would improve the ranking overall if team members' experience and knowledge were described in greater detail.

    Feasibility

    The idea, which uses a trained deep neural network to scan photos for anemia detection, is theoretically and practically possible. Using a deep learning algorithm trained on a solid dataset of more than 25,000 photos, the technological approach is sound. Nevertheless, by offering more thorough technical blueprints, probable obstacles, and mitigation techniques, the viability could be further confirmed.

    Viability

    While the concept is viable and the technology appears solid, the proposal falls short in detailing the team's expertise and experience. This is crucial for assessing the likelihood of successful project implementation. Additionally, the timeline and budget seem reasonable, but a more detailed breakdown of tasks and timelines would improve confidence in the project's viability.

     

    Desirability

    The project has great desirability. Since anemia is a serious worldwide health concern, there is a critical need for a non-invasive, affordable remedy. Because it fills a significant need, the app's design for use in underserved and rural places makes it a highly valued initiative. Its attractiveness is further enhanced by the possibility of extending its use to additional medical diagnostics.

    Usefulness

    The app's usefulness is substantial, as it not only addresses a major health challenge but also promotes the practical use of AI in healthcare. By reducing the need for invasive procedures, it enhances patient comfort and safety while also being scalable for mass screenings. The integration with health management systems ensures immediate data availability, which is a significant advantage.

  • 0
    user-icon
    Devbasrahtop
    May 25, 2024 | 3:57 AM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 5
    • Usefulness 5
    AI Health App Aims to Transform Anemia Screening

    Overall

    AI-powered anemia screening is proposed as a revolutionary solution in the SDG3 health app by Ameya Life. The endeavor to provide non-invasive, real-time hemoglobin testing is praiseworthy and fits in nicely with the objectives of global health. The initiative is creative and has the potential to make a big difference, especially in underprivileged communities. Unfortunately, the proposal is devoid of specific information about the team's qualifications, which is essential for determining the project's feasibility. It would improve the ranking overall if team members' experience and knowledge were described in greater detail.

    Feasibility

    The idea, which uses a trained deep neural network to scan photos for anemia detection, is theoretically and practically possible. Using a deep learning algorithm trained on a solid dataset of more than 25,000 photos, the technological approach is sound. Nevertheless, by offering more thorough technical blueprints, probable obstacles, and mitigation techniques, the viability could be further confirmed.

    Viability

    While the concept is viable and the technology appears solid, the proposal falls short in detailing the team's expertise and experience. This is crucial for assessing the likelihood of successful project implementation. Additionally, the timeline and budget seem reasonable, but a more detailed breakdown of tasks and timelines would improve confidence in the project's viability.

     

    Desirability

    The project has great desirability. Since anemia is a serious worldwide health concern, there is a critical need for a non-invasive, affordable remedy. Because it fills a significant need, the app's design for use in underserved and rural places makes it a highly valued initiative. Its attractiveness is further enhanced by the possibility of extending its use to additional medical diagnostics.

    Usefulness

    The app's usefulness is substantial, as it not only addresses a major health challenge but also promotes the practical use of AI in healthcare. By reducing the need for invasive procedures, it enhances patient comfort and safety while also being scalable for mass screenings. The integration with health management systems ensures immediate data availability, which is a significant advantage.

  • 0
    user-icon
    mivh1892
    May 23, 2024 | 12:03 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Revolutionizing Anemia Screening with AI

    The "SDG3 Health App Revolutionizes Anemia Screening with AI" project exhibits high feasibility, sustainability, desirability, and usefulness. However, a clear business model and long-term development strategy are necessary to ensure project sustainability.

    Feasibility:

    • Need: Anemia is a global health problem affecting over 1.5 billion people, particularly severe in developing regions. The demand for an effective and accessible screening method is high.
    • Solution: The app utilizes AI and retinal imaging to provide non-invasive, convenient, cost-effective, and scalable anemia screening.
    • Competitiveness: The solution differentiates itself from traditional screening methods, offering high accuracy and ease of use.

    Sustainability:

    • Business Model: A clear monetization model for the project is not explicitly mentioned.
    • Barriers to Entry: Financial and human resources are required to develop, maintain, and promote the app.
    • Adaptability: AI algorithms and technological trends need to be updated to maintain effectiveness.

    Desirability:

    • Market Need: The demand for an effective and accessible anemia screening solution is growing.
    • Urgency: Addressing anemia is crucial to improve public health and reduce economic burdens.
    • Community Interest: Potential exists to attract involvement and support from healthcare and community organizations.

    Usefulness:

    • Solves a Real Problem: Provides a rapid, accurate, non-invasive, and cost-effective anemia screening method.
    • Creates Value for Users: Enhances access to anemia diagnosis and treatment.
    • Social Impact: Contributes to improved public health and reduced anemia mortality rates.

  • 0
    user-icon
    Tu Nguyen
    May 22, 2024 | 10:32 AM

    Overall

    4

    • Feasibility 3
    • Viability 4
    • Desirabilty 3
    • Usefulness 4
    SDG3 Health App: Advancing Global Well-Being

    This proposal will solve the problem of anemia. It is a global health challenge that affects more than 1.5 billion people, leading to severe public health consequences and economic burden, especially in developing regions. Current methods for diagnosing anemia are largely invasive, requiring blood sampling, posing significant challenges in terms of accessibility, cost, discomfort, and risk of infection. The solution of this proposal is to use an AI-powered mobile health application to perform non-invasive screening using images of the lower eyelid conjunctiva. The app uses a deep learning algorithm trained on more than 25,000 data points, allowing it to analyze eye images to accurately estimate hemoglobin levels. This is a useful solution in practice.
    However, I have some ideas that I hope will be useful to them. They should share information about members more clearly in the Project Team section. They should define a more detailed budget based on milestones. And they should define the start and end times of each milestone.

  • 0
    user-icon
    Joseph Gastoni
    May 21, 2024 | 4:13 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    an AI-powered mobile app

    This proposal outlines an AI-powered mobile app for non-invasive anemia screening using images of the lower eyelid. Here's a breakdown of its strengths and weaknesses:

    Feasibility:

    • High: The technology leverages existing smartphone camera technology and deep learning algorithms, making it potentially feasible to develop.
    • Strengths: The focus on readily available technology and a well-defined training dataset suggests a clear development path.
    • Weaknesses: The accuracy of the deep learning model for real-world scenarios needs validation through clinical trials.

    Viability:

    • Moderate: Success depends on regulatory approval, adoption by healthcare providers, and integration with existing healthcare systems.
    • Strengths: The non-invasive approach and potential cost-effectiveness offer a compelling value proposition for resource-limited settings.
    • Weaknesses: The proposal lacks details on the business model for app distribution, maintenance, and potential revenue streams.

    Desirability:

    • High (potential): For healthcare providers in resource-limited settings and individuals seeking a convenient anemia screening option, this app can be desirable.
    • Strengths: The focus on non-invasive, user-friendly, and potentially low-cost screening addresses a significant need.
    • Weaknesses: The proposal needs to address concerns about user trust in the accuracy of an AI-based diagnostic tool.

    Usefulness:

    • High (potential): This app has the potential to improve access to anemia screening in underserved areas and streamline healthcare delivery.
    • Strengths: The non-invasive approach could increase screening rates and early detection of anemia.
    • Weaknesses: The proposal lacks details on how the app will integrate with existing healthcare workflows and ensure follow-up care for diagnosed individuals.

    Competition and USPs:

    • The app's unique features include non-invasive screening using smartphone technology and a focus on accessibility in underserved areas.
    • However, the proposal needs to address competition from existing point-of-care anemia tests, some of which may be established in target markets.

    Overall, the SDG3 Health App presents a promising approach to improving access to anemia screening. However, focusing on clinical validation, a well-defined business model, and user education about the technology can significantly improve its effectiveness. By addressing these points and establishing partnerships with relevant stakeholders, this app has the potential to make a significant impact in global health.

    Here are some strengths of this project:

    • Addresses a critical health issue in underserved areas: anemia screening and detection.
    • Offers a potentially low-cost, non-invasive, and user-friendly solution using existing smartphone technology.
    • Focuses on scalability and adaptability for broader applications in global health diagnostics.

    Here are some challenges to address:

    • Clinical validation of the deep learning model's accuracy in real-world settings.
    • Securing regulatory approval and navigating the healthcare landscape in target markets.
    • Developing a sustainable business model for app distribution, maintenance, and potential revenue streams.

Summary

Overall Community

4

from 12 reviews
  • 5
    1
  • 4
    10
  • 3
    1
  • 2
    0
  • 1
    0

Feasibility

3.8

from 12 reviews

Viability

3.9

from 12 reviews

Desirabilty

3.9

from 12 reviews

Usefulness

4.2

from 12 reviews

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