1. Ai.D Join the battle against one of the deadliestdiseases today… Ntua Team

2. The Challenge

3. Malaria is responsible annually for: 250,000,000 infections 1,000,000deaths *WHO, malaria report 2008

4. Africa: 90% of total deaths 88% children <5 *WHO, malaria report 2008

5. The reasons Flu like symptoms Few Doctors 2/10.000 61% Rural population EXAMINATION DIAGNOSIS TREATMENT

6. AiD approach FrequentExaminations local station Doctors Network mobile phones Organize Information Maps and medical data EXAMINATION DIAGNOSIS TREATMENT

8. Access all stations

10. Doctors Network AiD Mobile Medical Coordinator Patient Local Doctors and NGOs Diagnosis AiD Station AiD Server AiD Observer

11. Ai.D algorithm

12. Doctors Network AiD Mobile Medical Coordinator Patient Local Doctors and NGOs Diagnosis AiD Station AiD Server AiD Observer

13. Doctors Network AiD Mobile Medical Coordinator Patient Local Doctors and NGOs Case Confirm AiD Station AiD Server AiD Observer

14. Doctors Network AiD Mobile Medical Coordinator Patient Local Doctors and NGOs All Cases AiD Station AiD Server AiD Observer

15. Final Diagnosis Help in 24h Initial diagnosis Technologies Clinical Diagnosis AiD Mobile AiD Station AiD Observer

16. 0.20$ per examination 500$ per station 0 $ doctors’ fee 1,300,000$ Estimated Cost 688,000,000$for malaria in Africa* *WHO, malaria report 2008

17. Imagine Cup Criteria Consistency Innovation The problem Impact Effectiveness User Experience Complexity Management Extendibility Functional Completeness

18. Imagine Cup Criteria Consistency Innovation The problem Impact Effectiveness User Experience Complexity Management Extendibility Functional Completeness

19. Imagine Cup Criteria Consistency Innovation The problem Impact Effectiveness User Experience Complexity Management Extendibility Functional Completeness

20. Imagine Cup Criteria Consistency Innovation The problem Impact Effectiveness User Experience Complexity Management Extendibility Functional Completeness

21. Imagine Cup Criteria Consistency Innovation The problem Impact Effectiveness User Experience Complexity Management Extendibility Functional Completeness

22. Imagine Cup Criteria Consistency Innovation The problem Impact Effectiveness User Experience Complexity Management Extendibility Functional Completeness

23. Imagine Cup Criteria Consistency Innovation The problem Impact Effectiveness User Experience Complexity Management Extendibility Functional Completeness

24. Imagine Cup Criteria Consistency Innovation The problem Impact Effectiveness User Experience Complexity Management Extendibility Functional Completeness

25. Imagine Cup Criteria Consistency Innovation The problem Impact Effectiveness User Experience Complexity Management Extendibility Functional Completeness

26. “…the capability of the system to automatically capture and analyze multiple images of the blood slide can save considerable time in diagnosing malaria since this procedure could take up to 40’ even for trained physicians” E. Dotsika, Dir. Cellular Immunity Lab, Pasteur Institute “…the existence of such a system in remote areas could send series of photos of blood smears to trained physicians that will make the final selection of the malaria cases” N. Vakalis, Prof. of Tropical Diseases, Former WHO member “…the project could actually have an impact on the early diagnosis of malaria, especially in remote areas and marginalized communities in the developing world. It would be worth trying to study its possible adoption and use by national and/or local health systems” G. Kouvaras, Country Director, ActionAid Hellas

27. Thank you Photos by: lyric1507, James Jordan

28. Q & As

31. medical staff

32. severe symptoms

34. HOW DO WE PRIORITIZE THE MALARIA CASES The identified malaria cases are prioritized as to their severity based on the counting of the isolated infected blood cells from the malaria parasite. So an image depicting a high number of infected blood cells is given high priority in relation to another.

35. SUPPORT OF THE HL7 STANDARD HL7 is a well established standard implemented by the majority of medical systems today. Ai.D is still in its proof-of-concept phase so we have chosen not to implement HL7 at this stage. However, we have used ICD10 in the categorisation of malaria cases in the Ai.D Mobile Appilcation.

36. TESTING OF Ai.D We have visited the two biggest and most esteemed medical centers in Greece, Evaggelismos and Ygeia Therapeutirion. There, we discussed our approach with the directors of the hematology department, especially Dr. Vakalis, professor of tropical diseaces and former WHO member. However, as in Greece malaria is extinct, we did not have the capability to test our system with suspected malaria patients.

37. OTHER SOLUTIONS AVAILABLE With regard to photographing and transmitting blood sample images, there is a wide selection of medical equipment available by companies such as Olympus, Leica, Cella Vision etc. However, this equipment costs tenths of thousands of dollars. Our system is much much cheaper, with the innovation of combining such characteristics with the capability of providing through our algorithm an initial analysis of the blood sample to identify suspected malaria cases.

38. CREATE AND SUSTAIN THE DOCTORS’ NETWORK We plan to contact primarily doctors organisations and NGOs acting in the Africa region. We have already set up a site in which such organisations can log in and register their doctors’ list. Following, our website alerts each doctor individually, providing him with the necessary credentials to download the mobile application and the PC client, so that he can start contributing to the network.

39. DOCTORS NEEDED 250.000.000 malaria cases are identified each year, meaning 685.000 cases per day. An initial target would be a 10% of this figure, meaning around 70.000 cases per day. A doctor of the Ai.D network could easily perform an average of 5 diagnoses per day, spending about ten minutes of his free time, meaning that we need a minimum of about 15.000 active doctors each day.

40. BUSINESS PLAN / BUSINESS MODEL We need about 100.000 to finalise our system in the next three months We target around 70.000 diagnoses per day, each station can perform an average 100 diagnosis per day, meaning we need a minimum of 700 stations, With a rough station cost of 500 $, 700 stations will cost about 350.000$ 150.000 $ to communicate and deploy the idea to 3-4 countries 50.000 $ to approach initially doctors networks and NGOs to start using our network Total of 650.000 $

41. BUSINESS PLAN / BUSINESS MODEL According to our rough estimations, we are probably going to need: 100.000 $ to finalise our approach. 150.000 $ to spread to 3-4 countries 50.000 $ to approach initially doctors networks and NGOs to start using our network 1.000.000 $ to set up around 700-1000 Ai.D stations

42. VIABILITY OF BUSINESS PLAN(LOCAL LEVEL) Imagine Ideas tend to attract considerable exposure. For example, Smart Eyes, a former Greek Imagine Cup Idea, has received a fund of 500.000 € by COSMOTE. Furthermore, national funding opportunities are available and NTUA has already much experience in supporting and promoting such ideas through corresponing innovation projects. Ai.D has already attracted considerable attention through publications in big newspapers and media.

44. MAKING THE SMEAR

45. FIXING

46. STAININGTOTAL TIME: 20 min approximately AUTOMATED PREPARATION