feat: add usability

siemdejong · Apr 19, 2023 · f9b26eb · f9b26eb
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diff --git a/README.md b/README.md
@@ -350,10 +350,10 @@ Yet to be adapted to this study.
             - [ ] results of analysis on performance errors
         - [ ] Model updating (performance per update)
         - [ ] Usability
-            - [ ] how and when in the clinical pathway to use the prediction AI
-            - [ ] how will the AI be integrated into the target setting + requirements (on-/offsite)
-            - [ ] how will poor data be assessed when implementing AI model
-            - [ ] any human interaction needed for data to be used with the model + expertise of users
+            - [x] how and when in the clinical pathway to use the prediction AI
+            - [x] how will the AI be integrated into the target setting + requirements (on-/offsite)
+            - [x] how will poor data be assessed when implementing AI model
+            - [x] any human interaction needed for data to be used with the model + expertise of users
         - [ ] Sensitivity analysis
           - [ ] Multiple splits
           - [ ] identify input that increase output uncertainty

diff --git a/pediatric-brain-tumours/sections/results/usability.tex b/pediatric-brain-tumours/sections/results/usability.tex
@@ -0,0 +1,13 @@
+\subsection{Usability}
+The prediction model can be used intraoperatively to predict tumour type and amount in a biopt.
+The biopt can be placed on the scanner as in [ref sylvia] and optionally the location of the tumour can be given in natural language.
+The model outputs a prediction in seconds.
+
+To integrate the model with the target system, the raw data needs to be converted to images of \qty{0.2}{mpp} for the model to accept it.
+A user interface should be designed with an optional user input for clinical context.
+The all tumours the model has been trained on with their probabilities should be displayed as output.
+The min-max-normalized attention map should be displayed along the prediction, optionally with a variable threshold.
+
+Data polluted with blood or a malfunctioning imaging system are not detected by the model.
+The user should proceed with caution if any of such artifacts appear.
+The model is shown to be accurate for images with the blood artifact where there are also regions of high quality [fig].