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Use-cases of Machine Learning in
Healthcare
Felipe Campos Kitamura, MD, MSc, PhD
Escola Paulista de Medicina
DEPARTAMENTO DE DIAGNÓSTICO 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
My current focus
15
16
19
Video de object detection
19.05.21 20
Artificial Intelligence is the new electricity
21
Andrew Ng
22
23
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
28
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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Use Cases Machine Learning for Healthcare

Editor's Notes

  1. Começou a ser desenvolvido no final de Abril 2020. U-net simples para segmentação. Saída do algoritmo em 1280 x 1280 é redimensionada para 512x512 (DICOM) e sobrepõem na imagem do exame. Análise do exame em janela C:-550 e W:1600 É gerado um gráfico com o volume total do pulmão e a % de cada classificação de alteração usando pixel spacing. Treinamento: 198 séries, 45.947 slices Novas versões são realizadas frequentemente, adicionando-se mais imagens no treinamento para melhorar a performance, sendo esta a versão 4 (em 19/05/2020) Origem e data das imagens do treino: imagem apenas do Santa Paula, mês de abril Análise do valor dos pixels em UH na área segmentada. São definidas 5 classes, e os pixels são coloridos baseado na sua classificação: Pulmão 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) Consolidação: -100 a +50 UH (Vermelho) A inferência só é feita nas séries que possuem como Series Description “mediastinobody1.0” do Santa Paula, em tempo real, dos exames comm 3 mm de espessura. Se não for 3mm, o exame é convertido para 3mm p/ inferência. A saída é enviada ao Carestream em três séries: inferência original com a máscara sobrepondo a área segmentada, segmentação 3D coronal da máscara, e gráfico com os % de vidro fosco. Retorna um JSON com os resultados para o monitoramento. algoritmo faz parte de um algoritmo de prognóstico, que será realizado em associação com 14 instituições. Já possuímos os dados de 700 pacientes (clínicos 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 complicações  • Previsão de progressão para doença crítica • Potencial para melhorar o desempenho dos radiologistas juniores ao nível sênior • Pode ajudar na avaliação dos efeitos do tratamento medicamentoso com quantificação por TC
  2. de um algoritmo de prognóstico, que será realizado em associação com 14 instituições. Já possuímos os dados de 700 pacientes (clínicos 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 complicações  • Previsão de progressão para doença crítica • Potencial para melhorar o desempenho dos radiologistas juniores ao nível sênior • Pode ajudar na avaliação dos efeitos do tratamento medicamentoso com quantificação por TC