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Use-cases of Machine Learning in
Healthcare
Felipe Campos Kitamura, MD, MSc, PhD
Escola Paulista de Medicina
DEPARTAMENTO DE DIAGN?STICO POR IMAGEM
1
Who am I?
? Learning to code since 13yo
? Medical Doctor (Radiologist) background
? Master of Science in Genetics and Biotech
? PhD in Artificial Intelligence in Radiology
? Machine Learning Practitioner
? Head of AI at Dasa
My research
interests
? Electronics (digital and analogical)
? Microcontroller programmer
? Digital Signal Processing
? Medical Devices
? Medical Imaging
? Image Processing
? Computer Vision
? AI, ML, DL
? Research to Practice
My work
My work
AI in Medicine
My current focus
AI in Medicine
AI in Medicine
AI in Medicine
15
16
AI in Medicine
AI in Medicine
19
Video de object detection
17.05.21 20
Artificial Intelligence is the new electricity
21
Andrew Ng
22
23
AI in Medicine
Surgical Fingerprints: Real-Time Analysis and
Summarization of Intraoperative Events
Daniel Hashimoto, MD, Edward D. Churchill Surgical Education and
Simulation Research Fellow, MGH
25
I¡¯m embarassed to tell you that,
but I see a robot therapist
AI in Medicine
28
AI in Medicine
AI in Medicine
AI in Medicine
COVID-19
7 passos
COVID19 e IA
7 passos
Covid19 e IA
35
36
Radiologists who use AI will replace those who don¡¯t
37
https://teachablemachine.withgoogle.com/train

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AI in Medicine

Editor's Notes

  • #34: Come?ou a ser desenvolvido no final de Abril 2020. U-net simples para segmenta??o. Sa¨ªda do algoritmo em 1280 x 1280 ¨¦ redimensionada para 512x512 (DICOM) e sobrep?em na imagem do exame. An¨¢lise do exame em janela C:-550 e W:1600 ? gerado um gr¨¢fico com o volume total do pulm?o e a % de cada classifica??o de altera??o usando pixel spacing. Treinamento: 198 s¨¦ries, 45.947 slices Novas vers?es s?o realizadas frequentemente, adicionando-se mais imagens no treinamento para melhorar a performance, sendo esta a vers?o 4 (em 19/05/2020) Origem e data das imagens do treino: imagem apenas do Santa Paula, m¨ºs de abril An¨¢lise do valor dos pixels em UH na ¨¢rea segmentada. S?o definidas 5 classes, e os pixels s?o coloridos baseado na sua classifica??o: Pulm?o normal: -1024 a -750 UH (Azul) Vidro fosco 1: -750 a -600 (Verde) Vidro fosco 2: -600 a -350 UH (Amarelo) VIdro fosco 3: -350 a -100 UH (Laranja) Consolida??o: -100 a +50 UH (Vermelho) A infer¨ºncia s¨® ¨¦ feita nas s¨¦ries que possuem como Series Description ¡°mediastinobody1.0¡± do Santa Paula, em tempo real, dos exames comm 3 mm de espessura. Se n?o for 3mm, o exame ¨¦ convertido para 3mm p/ infer¨ºncia. A sa¨ªda ¨¦ enviada ao Carestream em tr¨ºs s¨¦ries: infer¨ºncia original com a m¨¢scara sobrepondo a ¨¢rea segmentada, segmenta??o 3D coronal da m¨¢scara, e gr¨¢fico com os % de vidro fosco. Retorna um JSON com os resultados para o monitoramento. algoritmo faz parte de um algoritmo de progn¨®stico, que ser¨¢ realizado em associa??o com 14 institui??es. J¨¢ possu¨ªmos os dados de 700 pacientes (cl¨ªnicos e laboratoriais). O objetivo desse algoritmo e avaliar o desfecho (todos os aspectos de morbi-mortalidade), levando a um tratamento mais precoce dos casos que apresentam maior chance de complica??es ?? Previs?o de progress?o para doen?a cr¨ªtica ? Potencial para melhorar o desempenho dos radiologistas juniores ao n¨ªvel s¨ºnior ? Pode ajudar na avalia??o dos efeitos do tratamento medicamentoso com quantifica??o por TC
  • #35: de um algoritmo de progn¨®stico, que ser¨¢ realizado em associa??o com 14 institui??es. J¨¢ possu¨ªmos os dados de 700 pacientes (cl¨ªnicos e laboratoriais). O objetivo desse algoritmo e avaliar o desfecho (todos os aspectos de morbi-mortalidade), levando a um tratamento mais precoce dos casos que apresentam maior chance de complica??es ?? Previs?o de progress?o para doen?a cr¨ªtica ? Potencial para melhorar o desempenho dos radiologistas juniores ao n¨ªvel s¨ºnior ? Pode ajudar na avalia??o dos efeitos do tratamento medicamentoso com quantifica??o por TC