The project aims to test, evaluate, and validate the use of artificial intelligence in diagnosing renal abnormalities in abdominal medical images. The project will apply a previously trained AI model to a new dataset of approximately 3,000 images to improve the model's ability to detect rare anomalies. Main benefits include early disease detection for patients, decision support and time savings for healthcare professionals, and cost reductions for healthcare organizations.

1. 22
NTT DATA
Identification of renal abnormalities
Project summary
Diagnosis based on Medical Image: Leveraging on Artificial Intelligence

2. The objective is to test, evaluate and validate the use of Artificial Intelligence in the
diagnosis of abdominal radiological medical imaging with a focus on the identification of
renal anomalies.
The project starts from an AI model to detect abnormalities in the kidney from abdominal
radiological images previously trained in different worldwide geographies. Thus, we aim to
apply and deploy this previously trained model on a data set of approximately 3000 images
in order to train it with a greater number of cases and types of anomalies with low
prevalence rates and thus achieve success rates that allow the use of this type of
algorithms as support for the decision of professionals.
Main benefits for stakeholders include:
• For patients: Early detection of diseases.
• For Healthcare professionals: CDSS, Risk reduction in the omission of abnormalities, Time to diagnosis
reduction.
• For Healthcare organizations: Time-to-diagnosis reduction (cost reduction), Tele-diagnosis based on AI. 23
NTT DATA
Identification of renal abnormalities
Project summary

3. NTT DATA
Identification of renal abnormalities
T1: Access and availability of medical image data including regulatory and ethical issues
1.- Data identification
and Anonymization: Drop Metadata
from DICOM images.
2.- Approximately 3.000 Studies
available.
4.- Covering a representative number of
abnormalities and comprising a good
balance between no-abnormal and
abnormal studies.
24

4. NTT DATA
Identification of renal abnormalities
T1: Access and availability of medical image data including regulatory and ethical issues
25
1.- Data Anonymization: Drop Metadata
from DICOM images is critical while
addressing GDPR and ethical constraints
and national/regional regulations.
2.- Need to put in place Data Management
plan and Data Protection Mechanisms.
• Data processing Document
• Risk Assessment Document
• DMP
• DP course (Mandatory for NTT DATA
Staff)
3.- Ethical Committee approval

5. 3 types of AI models trained:
• Inception: trained to classify kidney images into normal and abnormal.
• LSTM Left and Right Kidney: trained to classify entire kidneys into normal and
abnormal. LSTMs (Long Short-Term Memory) are advanced RNNs (recurrent neural
networks) that can handle long term dependencies.
NTT DATA
Identification of renal abnormalities
T2: Evolution of implementation and validation of AI-bases models and CDSSs issues
26

6. By comparing the results of all LSTM LK models trained, we can observe that the best
overall model on the data provided by the data provider organization is US2-C (blue), when
the US2 model was fine tuned for this data.
For LSTM RK, the US2-C model (blue) is a clear option with minimal differences in case of
Precision N and Specificity.
NTT DATA
Identification of renal abnormalities
T3: Explainable AI and interpretation. Ethical AI
27

7. Impact of QA annotation on AI training: A general improvement of all models can be
observed, with the biggest difference being observed for Specificity.
Conclusion: Validation of AI based on the expertise of radiologists (or clinical experts in
the scope of the speciality we are dealing with in general) is fundamental to improve AI
performance in the scope of Health.
NTT DATA
Identification of renal abnormalities
T3: Explainable AI and interpretation. Ethical AI
28

8. 1. Appropriate Data Processing and Data anonymization is critical to
obtain the ethical approval from health organizations committees.
2. Data processing is the most time-consuming task. Gaining access to
quality data contributes to speed up this task.
3. QA validation from healthcare professionals is required for the AI
validation.
4. CE marking and AI explainability together with a suitable strategy in
change management is required for the adoption of AI in clinical
settings. 29
NTT DATA
Identification of renal abnormalities
T4. Good Machine Learning Practices, Impact and exploitation