Integration of a Deep Learning-Based Module for the Quantiﬁcation of

Imaging Features into the Filling-in Process of the Radiological

Structured Report

Camilla Scapicchio

1,2 a

, Elena Ballante

3,4

, Francesca Brero

, Raffaella Fiamma Cabini

4,5

Andrea Chincarini

, Maria Evelina Fantacci

1,2

, Silvia Figini

3,4

, Alessandro Lascialfari

Francesca Lizzi

, Ian Postuma

and Alessandra Retico

2 b

Department of Physics, University of Pisa, Pisa, Italy

National Institute for Nuclear Physics (INFN), Pisa, Italy

Department of Political and Social Sciences, University of Pavia, Pavia, Italy

National Institute for Nuclear Physics (INFN), Pavia, Italy

Department of Mathematics, University of Pavia, Pavia, Italy

National Institute for Nuclear Physics (INFN), Genova, Italy

Keywords:

COVID-19, Computed Tomography, Deep Learning, Quantiﬁcation Software, Structured Report.

Abstract:

The role of Computed Tomography (CT) in the characterization of COVID-19 pneumonia has been widely

recognized. The aim of this work is to present the idea of integrating a Deep Learning (DL)-based software,

able to automatically quantify qualitative information typically describing COVID-19 lesions on chest CT

scans, into a structured report-ﬁlling pipeline. Different studies have highlighted the value of introducing the

use of structured reports in clinical practice, as a reproducible instrument for diagnosis and follow-up rather

than the commonly used free-text radiological report. Structured data are fundamental to helping clinical de-

cision support systems and fostering precision medicine. We developed a Deep Learning based software that

segments both the lungs and the lesions associated with COVID-19 pneumonia on chest CT scans and quan-

tiﬁes some indexes describing qualitative characteristics used to assess COVID-19 lesions clinically. Once

assessed the robustness of the system by means of a multicenter clinical evaluation made by clinical experts, it

can be used for the ﬁrst stratiﬁcation of patients, supporting radiologists with a computer-aided quantiﬁcation,

and the derived quantities, immediately intelligible for the clinicians, are suitable to be inserted in a structured

report in COVID-19 pneumonia and then exploited as explainable features to build predictive models.

ABBREVIATIONS

AI, Artiﬁcial Intelligence; AUC, Area Under the

ROC-Curve; CNN, Convolutional Neural Network;

CT, Computed Tomography; CTSS, CT Severity

Score; DL, Deep Learning; ESR, European Soci-

ety of Radiology; GG, Ground Glass; P, Percentage;

RT-PCR, Reverse Transcription Polymerase Chain

Reaction; sDSC, surface Dice similarity coefﬁcient;

SIRM, Italian Society of Medical Radiology; TCIA,

The Cancer Imaging Archive; vDSC, volumetric Dice

similarity coefﬁcient.

https://orcid.org/0000-0001-5984-0408

https://orcid.org/0000-0001-5135-4472

1 INTRODUCTION

Medical imaging has been proven to have a role in

the characterization of COVID-19 pneumonia and in

the assessment of the severity of the disease (Kol-

lias et al., 2022). In particular, chest Computed To-

mography (CT) is typically used for the management

of COVID-19 patients (Rubin et al., 2020). Both

qualitative and quantitative chest CT indicators can

be used to assess the severity of COVID-19 pneu-

monia (Lyu et al., 2020). The main typical features

have been summarized in different reports (Carotti

et al., 2020). Different software tools based on Deep

Learning (DL) have been developed to automate the

segmentation of the COVID-19 lesions (Zhao et al.,

2021), (Mergen et al., 2020), from which the quanti-

Scapicchio, C., Ballante, E., Brero, F., Cabini, R., Chincarini, A., Fantacci, M., Figini, S., Lascialfari, A., Lizzi, F., Postuma, I. and Retico, A.

Integration of a Deep Learning-Based Module for the Quantiﬁcation of Imaging Features into the Filling-in Process of the Radiological Structured Report.

DOI: 10.5220/0011921900003414

In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF, pages 663-670

ISBN: 978-989-758-631-6; ISSN: 2184-4305

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

663

tative indicators can be extracted. However, there are

very few studies that explore the ability of automated

DL software to quantify intelligible qualitative infor-

mation from the segmented regions.

The Italian Society of Medical Radiology (SIRM)

has shown its interest in the standardization of the ra-

diological report to reduce the variability of free-text

radiological reports and improve the workﬂow in clin-

ical routine (Faggioni et al., 2017). A structured re-

port is a standardized template with predeﬁned ﬁelds

describing different kinds of information related to di-

agnostic imaging. The European Society of Radiol-

ogy (ESR) recognized also the potential of structured

reports, especially in facilitating data sharing and data

mining thanks to the use of key data elements and

quantiﬁed parameters (of Radiology (ESR) commu-

nications@ myesr. org, 2018). This new trend for the

integration of structured reports in radiology has been

widely reviewed in its advantages and potentialities

(Rocha et al., 2020). The need for this standardized

reporting scheme has been given a new emphasis with

the spreading of the COVID-19 pandemic (Neri et al.,

2020), (

Ozer et al., 2021), (Salvatore et al., 2021).

The aim of this work is to develop an automated

tool able to provide qualitative descriptive metrics

characterizing COVID-19 lesions and include these

metrics in a structured report of chest CT in COVID-

19 pneumonia, to support clinicians in the manage-

ment of patients. We selected 4 qualitative parame-

ters describing COVID-19 lesions that were automat-

ically quantiﬁed by the software starting from the seg-

mentations: the CT severity score (CTSS), the lesion

type, the bilateral involvement, and the basal predom-

inance. These metrics have been chosen because they

are the ones commonly visually assessed by radiolo-

gists in the routine clinical practice of chest CT visual

evaluation.

The possibility to obtain from a DL-based seg-

mentation software qualitative intelligible features,

immediately understandable by clinicians, makes the

system more explainable and trustworthy. Moreover,

these quantiﬁed qualitative features could be directly

included in a structured report, giving the clinicians

support in the assessment of the characterization of

the pathology.

There are other related works in literature focused

on DL systems applied to chest CT scans in COVID-

19 pneumonia (Colombi et al., 2020), (Fervers et al.,

2022), (Caruso et al., 2021). However, their main lim-

itation is that their aim is not to use the DL system

to quantify the same qualitative parameters clinically

relevant to characterize the pathology as the ones we

take into account in our study. They nor consider the

idea of using the system as an aid for the ﬁlling in of

the radiological structured report.

2 MATERIALS AND METHODS

2.1 Dataset

For the training and test of the updated version of the

DL-based segmentation software considered in this

study, the same datasets and their partitions in a train,

validation, and test sets used in the baseline version

described in (Lizzi et al., 2022) have been adopted.

Whereas, to evaluate the ability of the developed

Deep-Learning software in the automatic quantiﬁca-

tion of the qualitative features characterizing COVID-

19 lesions, a subset of a speciﬁc public dataset has

been used. This validation has been made by means

of a statistical comparison between the output of the

software and multicenter clinical evaluations. There-

fore, it is composed of only 120 CT scans to not over-

load clinicians’ work. They were sampled from the

TCIA database (CT Images in COVID-19), which in-

cludes only patients with SARS-CoV-2 infection con-

ﬁrmed by Reverse Transcription Polymerase Chain

Reaction (RT-PCR). The images were randomly se-

lected so that they were not used in the training of the

DL-based software, but were sampled with severity

score statistics similar to the one used for the train-

ing of the software. However, the severity score is not

given as ground truth for the dataset, but it is inferred

from the output of the software. The distribution of

the CTSS (the severity score index ranging from 1 to

5, with 5 corresponding to the most severe cases) for

the 120 cases is 75 cases with CTSS=1, 36 cases with

CTSS=2, and 9 cases with CTSS=3. Images are in the

NIfTI ﬁle format and were fully anonymized, there-

fore the acquisition parameters and patient informa-

tion were not provided.

2.2 Deep Learning Software

In this study, we used an updated version of our cus-

tom software (Lizzi et al., 2022), which is a DL-based

pipeline for the segmentation and quantiﬁcation of

COVID-19 pulmonary lesions. It is based on a cas-

cade of three Convolutional Neural Networks. One

CNN is used to predict a bounding box enclosing the

lungs and two U-nets are devoted to the segmentation

of the lungs and of the COVID-19 lesions. The out-

put of the software is the lung parenchyma segmenta-

tion mask, the COVID-19 lesion segmentation mask,

including Ground Glass (GG) opacities and consoli-

dations (typical ﬁndings of COVID-19 disease) (Fig-

ure 1), the percentage P of lung volume affected by

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664

COVID-19 lesions and the CTSS, deﬁned as follows:

CTSS = 1 for P < 5%; CTSS = 2 for 5% ≤ P < 25%;

CTSS = 3 for 25% ≤ P < 50%; CTSS = 4 for 50% ≤

P < 75%; CTSS = 5 for P ≥ 75%.

This updated version of the algorithm underwent

the same training and testing procedure, in terms of

used datasets and hyperparameters, adopted for the

ﬁrst version and described in (Lizzi et al., 2022). The

update covered the addition of the ﬁrst of the three

CNNs, devoted to the identiﬁcation of a bounding box

enclosing the lungs performed through a regression.

It has been added to make the system work also on

CT images acquired with a different Field Of View.

Other updates are the introduction of a function that

separates the right and left lungs with two different

masks, and a threshold to differentiate consolidations

from GG in the lesion mask.

The metrics used to validate the segmentation per-

formance of this updated version were surface and

volumetric Dice similarity coefﬁcients (sDSC and

vDSC) computed between the segmented masks and

the reference ones. They were computed on the cases

of the same benchmark dataset used to test the ﬁrst

version of the algorithm (Lizzi et al., 2022). The

sDSC at 5 mm of tolerance and the vDSC for lung

segmentation are equal to 0.97 ± 0.01 and 0.96 ±

0.01, respectively. For the lesion segmentation, the

performance in terms of sDSC 5mm and the vDSC

are equal to 0.83 ± 0.07 and 0.69 ± 0.08, respectively.

The Mean Absolute Error in assessing the percentage

of the infected lung is equal to 2%. The accuracy in

assigning the correct CTSS class is equal to 80%. The

explanation of these evaluation metrics and the reason

for their adoption is reported in (Lizzi et al., 2022).

These results allow us to consider the software sta-

tistically validated in its segmentation performance

2.3 Quantiﬁcation of the Qualitative

Metrics

Once the DL-based system has been trained, it can be

used at the inference phase to obtain from a CT scan

volume the corresponding lung and lesions segmen-

tation masks and a set of volumetric estimates com-

puted on the masks. These raw volumetric outputs

are:

• Lung volume: total volume of the lungs.

• LL ratio: the ratio between the total volume of the

lesion and the total volume of the lungs.

• consolidation volume: volume of consolidations

in the lesion mask.

• lesion volume: total volume (right + left) of the

lesion (GG + consolidations).

• R gg: volume of GG in the right lung.

• L

gg: volume of GG in the left lung.

• L con: volume of consolidations in the left lung.

• R con: volume of consolidations in the right lung.

These values derived from the segmented region

of the COVID-19 lesion on the CT image can be ex-

ploited to quantify some qualitative features or met-

rics relevant to characterize COVID-19 pneumonia.

On the basis of common clinical knowledge as con-

sidered in the routine visual evaluation of chest CT

scans, the following quantiﬁable qualitative metrics,

with the correspondent categories, have been identi-

ﬁed.

1. Lesion Type:

• Ground Glass Only: only GG opacities are

present. GG appears as a hazy increase in

opacity of the lungs, with preservation of the

bronchial and vascular margins (Hansell et al.,

2008). It has been reported as the primary ﬁnd-

ing of COVID-19 pneumonia on CT scans.

• Mainly Ground Glass: most of the lesion is

GG, but scattered consolidation sites are also

present. Consolidations appear as a homoge-

neous increase in pulmonary parenchymal at-

tenuation that obscures the margins of the ves-

sels and airway walls (Hansell et al., 2008), and

they are typically associated to a more severe

prognosis (Carotti et al., 2020).

• Consolidation and GG: GG and consolidations

are present in approximately similar propor-

tions.

• Mainly consolidations: most of the lesion is

consolidation, but GG is also visible.

• Consolidations Only: only consolidations are

present.

There are no established thresholds to discrimi-

nate visually among the types of lesions.

This Lesion Type qualitative metric has been au-

tomatically translated into a quantitative index by

exploiting the output of the segmentation software

as follows:

LesionType =

consolidation volume

lesion volume

(1)

When this index is closer to zero, the lesion is

mainly GG; when it is closer to 1, the lesion is

mostly consolidation.

2. Bilateral distribution of the lesion: when pul-

monary lesions are visible in both lungs in an

approximately similar percentage (Abou Ghayda

et al., 2021). It can be described by a binary cate-

gorization yes/no.

Integration of a Deep Learning-Based Module for the Quantiﬁcation of Imaging Features into the Filling-in Process of the Radiological

Structured Report

665

Figure 1: Example of software output. Left: original input CT scan image with axial, coronal, and sagittal projections.

Center: lungs segmentation mask. Right: COVID-19 lesions segmentation mask, with different labels for GG (light orange)

and consolidations (dark orange).

This qualitative metric has been quantiﬁed into a

representatative index by combining the raw val-

ues obtained with the segmentation software by

means of the following formula:

Bilateral = 1 −

con

+ R

) − (L

con

+ L

)

lesion volume

(2)

The lower the index, the less bilateral the lesion.

3. Basal predominant distribution of the lesion:

when lesions affect mainly the bases of the

lungs with relative sparing of the upper regions

(Rizzetto et al., 2021).

The quantitative index corresponding to the basal

distribution is obtained by projecting both the

lung distribution and the lesion distribution on the

z-axis (the lung axis). The index value is cal-

culated as the percentile of the lung distribution

which lies the median of the lesion distribution.

A lower index corresponds to a lower z and there-

fore to a more basal distribution of the lesions.

4. CT Severity Score: a 5-class score describing the

lung compromised fraction (1=0-5%, 2=5-25%,

3=25-50%, 4=50-75%, 5=75-100%).

The translation of this visually estimated severity

index into a quantitative index has been obtained

by computing the percentage P of lung affected

by COVID-19 infection from the volumes of the

segmentation masks as:

P =

lesion volume

Lung volume

x100 (3)

and CTSS = 1 for P < 5%, CTSS = 2 for 5% ≤ P

< 25%, CTSS = 3 for 25% ≤ P < 50%, CTSS =

4 for 50% ≤ P < 75%, CTSS = 5 for P ≥ 75%.

2.4 Multicenter Evaluation

The 120 CT scans of the public dataset TCIA were

processed with the DL software to obtain the segmen-

tation masks and the volumetric values. We then de-

rived these quantitative indexes corresponding to the

qualitative metrics as described before, for each of the

120 cases, obtaining a table of values.

Before evaluating the possibility of using the soft-

ware as a support to the automatic ﬁlling in of the

structured report form, it is worth evaluating the reli-

ability of the system in this quantitative translation of

clinical qualitative metrics. In this case, we assessed

the performance of the DL algorithm by means of a

multicenter evaluation, which is a sort of clinical val-

idation. It was based on a statistical analysis of the

agreement between the software output and the vi-

sual assessments of 14 clinical experts from 5 clinical

centers on the deﬁned qualitative metrics for the de-

scribed public dataset of chest CT scans of COVID-19

patients. This comparison was performed with two in-

dependent statistical methods: an AUC analysis and a

non-linear regression based on a previous work (Chin-

carini et al., 2019). The details of this comparison are

the subject of another work under review and are not

the focus of the present study, therefore they are not

reported here.

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666

Figure 2: The scheme of the proposed pipeline of integration between the AI module and the radiologist’s assessment to ﬁll

in the radiological structured report for the management of COVID-19 patients.

2.5 AI Module Integration in

Structured Report Fulﬁllment

Different studies highlight that the main CT ﬁndings

that characterize COVID-19 lesions and are predic-

tors of patient outcome are those considered in the

present study, i.e. related to the type and location of

the lesion (Salvatore et al., 2021). Moreover, the pro-

posed structured report for COVID-19, deﬁned with

a consensus agreement, contains some ﬁelds, such

as Location (Bilateral, Left unilateral, Right unilat-

eral), Cranio-caudal distribution (Predominant in the

lower lobes, Predominant in the upper lobes, Multi-

focal/patching) for both Ground Glass Opacities and

Consolidations, and the volumes of GG and consol-

idations for right and left lung (Neri et al., 2020),

which precisely correspond to the qualitative imaging

parameters described in this study.

Therefore, the idea is to use the output of the soft-

ware to automatically ﬁll in the values for these ﬁelds

in the imaging sections of the structured report. This

could be the last step to clinically apply the developed

software, and it could be possible thanks to the bases

laid through the steps explained in the previous sec-

tions.

As shown in Figure 2, we propose a novel archi-

tecture that integrates an AI segmentation, classiﬁca-

tion, and quantiﬁcation module, previously described,

into the reporting pipeline of the structured scheme

for COVID-19 patients. The same CT scan acquired

from the patient undergoes both the radiologist’s vi-

sual examination and the AI module processing. The

radiologist ﬁlls in the clinical information part of the

report and the imaging information part for what con-

cerns the visual assessment of the deﬁned qualita-

tive parameters. The same imaging parameters are

obtained automatically from the segmentation soft-

ware and the following manipulation of its outcome,

as previously described. Thus, these obtained quanti-

tative indexes are automatically inserted into the cor-

responding ﬁeld of the qualitative metric in the struc-

tured report form.

The idea is also to add an automated matching al-

gorithm that compares the ﬁnal index of the AI mod-

ule with the radiologist’s annotation so that the radiol-

ogist can operate a second reading only on those cases

with discordant evaluations.

3 RESULTS

As previously outlined, one of the direct outputs

of the DL segmentation software is the CTSS. The

other qualitative metrics (Lesion Type, Bilateral dis-

tribution, and Basal distribution), conversely, are ob-

tained with manipulation of the other volumetric val-

ues given as output of the software by means of the

formulas described above. In Figure 3, an example

of a table of the qualitative indexes obtained with this

post-processing manipulation is reported.

Integration of a Deep Learning-Based Module for the Quantiﬁcation of Imaging Features into the Filling-in Process of the Radiological

Structured Report

667

Figure 3: Example of the table of quantitative values de-

scribing the qualitative metrics characterizing COVID-19

lesions, obtained from the output values of the segmenta-

tion software.

These indexes have been obtained for all 120 con-

sidered cases. The CT images have been processed

by the segmentation software, the volumes have been

computed on the segmented masks and the indexes

corresponding to the qualitative metrics have been

computed from them by means of the formulas re-

ported in Section 2.3.

To compare this automatic output to the visual

evaluation of clinicians, for each of the 120 scans, the

radiologists were asked to visually assess them and

assign a category to the four qualitative parameters

deﬁned in Section 2.3. As each clinician’s evalua-

tion is blind to that of the others, we can consider an

assumption of independence. Therefore, the “true”

evaluation was estimated as the mean of the clini-

cians’ opinions. These ”true” evaluations have been

compared to the indexes given automatically from the

software by means of an AUC analysis and a non-

linear regression. We do not discuss here the details

of this multicenter evaluation, as it is the subject of an-

other work under revision. However, this evaluation is

useful to state that the software is robust in the quan-

tiﬁcation of qualitative parameters and could be there-

fore used to support the ﬁlling-in of the structured re-

port. We report here, in Table 1, just the summarized

results demonstrating the reliability of the software.

Once the statistical comparison conﬁrms the ro-

bustness of the performance of the software, it can

be used to automatically quantify the qualitative fea-

tures on the CT scan. Therefore, the following step

is the integration of the segmentation software and

the post-processing computation of the quantitative

indexes (AI module). The proposed pipeline and inte-

gration scheme is shown in Figure 2. The implemen-

tation of this design with a corresponding interface to

use the complete package has yet to be developed, as

the research here presented is in progress.

Therefore the results here reported are intermedi-

ate as the methods have to be slightly reﬁned and val-

idated. However, the goal of this paper is to convince

the audience that the idea here described of an autom-

atized fulﬁllment of a structured report is valid and to

underline the need for further research in this direc-

tion.

4 DISCUSSION

We described an automatic pipeline to obtain a quan-

tiﬁcation of some qualitative parameters typically

used by clinicians to characterize COVID-19 lesions

on CT scans. After having evaluated the reliability of

the software by means of a multicenter evaluation and

a statistical analysis, we proposed the integration of

this AI system into the process of the structured report

fulﬁllment, as a fully-automated tool to support clini-

cians in the diagnosis and management of COVID-19

patients.

The statistical analysis of the comparison between

the quantiﬁcation tool outcome and the radiologists’

visual assessments of the chest CT scans of the con-

sidered public dataset shows that the software is able

to distinguish with acceptable precision among the

categories of the clinical metrics. In fact, as evi-

dent from Table 1, the AUC values are quite satisfac-

tory, and the cutoff values obtained with independent

methods – Youden index in AUC analysis and inﬂec-

tion point in non-linear regression model – are quite

comparable for the different qualitative metrics. This

allows us to consider the software a robust quantiﬁ-

cation system of these qualitative metrics describing

COVID-19 pneumonia and use it to automatically ﬁll

in the structured report. The use of this latter is largely

promoted nowadays to overcome the variability due

to the free-text radiological reports and harmonize the

communication of ﬁndings and diagnosis among dif-

ferent clinical centers.

The usefulness of the integration of an automated

AI-based tool in the fulﬁllment of a COVID-19 pa-

tient’s structured report is in the fact that the visual

assessment of this new disease from chest CTs is not

so trivial, especially for borderline cases. In fact, from

the multicenter evaluation, it turned out that there is a

huge heterogeneity of the clinical evaluations on sev-

eral cases of the public database (TCIA). This poor

agreement among radiologists’ opinions suggests that

these qualitative metrics are not easy to visually eval-

uate and especially quantify. This is because COVID-

19 imaging patterns are non-speciﬁc and a 3D vol-

ume quantiﬁcation by means of a 2D scrolling viewer

is not so trivial. Therefore, a quantitative aid with

automatic software can play a role in improving the

clinical workﬂow related to COVID-19 patients and

providing the necessary evaluation contrast in the in-

terpretation of borderline cases.

Moreover, the future addition of an automated

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668

Table 1: AUC, Youden’s cutoff and sigmoid-ﬁt inﬂection point on the software outputs vs the respective dichotomized clinical

metrics.

Metric AUC Youden cutoff Inﬂection linear constrain

[95% CL]

CTSS 0.98 0.10 0.20 [0.19 0.21]

Bilateral 0.85 0.60 0.64 [0.52 0.76]

Basal Predominant 0.90 0.34 0.32 [0.31 0.33]

Lesion Type 0.81 0.15 0.18 [0.12 0.25]

matching algorithm in the proposed pipeline of inte-

gration between the AI module and the radiologist’s

annotations could be used as further validation, and

following updating and improvement, of the software

itself.

The relevance of the present study consists of the

possibility to translate qualitative assessments charac-

terizing COVID-19 lesions into quantiﬁable metrics,

which, therefore, represent intelligible features imme-

diately understandable by clinicians. Through these

quantitative data, it is possible to build more com-

plex and structured datasets able to foster data mining

and precision medicine. This will enable the develop-

ment of predictive models exploiting radiomics and

Machine Learning, to foresee, for instance, if the pa-

tient will develop a severe progression of the pathol-

ogy. So, a structure report thus produced, with quan-

tiﬁed parameters, could be used for further analytic

research.

4.1 Limitations and Future Perspectives

A limitation of this study is the use of a public dataset

of CT scans designed for research purposes, with-

out information on the acquisition parameters, patient

metadata, or scanner type. Another limitation is due

to the fact that the sampling of the 120 cases used for

the validation was done on an imbalanced dataset in

terms of class representation for CTSS. This was due

to the imbalance in the original dataset used for the

software training. Another deﬁciency in our study is

that the ﬁelds in the report that can be automatically

ﬁlled in from the AI module are relatively limited in

terms of measurement content. The AI module is not

yet capable of presenting a complete imaging diagno-

sis report as some qualitative metrics are not included,

such as for example the peripheral distribution. The

future perspective is to extend the qualitative metrics

that the AI software is able to quantify, precisely start-

ing from providing a peripheral distribution index.

The future perspective will regard also the imple-

mentation and deployment of a speciﬁc interface for

this integrated tool, to use in a clinical scenario as a

decision support tool for healthcare providers, and in

the second instance, to provide researchers with struc-

tured and quantitative data. The idea is also to ex-

tend the architecture of this proposed framework to

other medical diseases whose diagnosis is based on

CT imaging.

ACKNOWLEDGEMENTS

This work has been carried out within the Ar-

tiﬁcial Intelligence in Medicine (next AIM,

https://www.pi.infn.it/aim) project funded by INFN

(CSN5) and within the FAIR-AIM project funded

by Tuscany Government (POR FSE 2014-2020).

We also thank the radiologists from the 5 clinical

centers (Universities hospitals of Florence (Azienda

Ospedaliero Universitaria Careggi), Milan (ASST

Grande Ospedale Metropolitano Niguarda), Palermo

(Azienda Ospedaliera Universitaria Policlinico “P.

Giaccone”), Pavia (Policlinico San Matteo Pavia

Fondazione IRCCS) and Pisa (Azienda Ospedaliero

Universitaria Pisana)) who provided the clinical

visual assessments for the multicenter evaluation part

of the study and anyone from the ﬁve clinical centers

cited in the text and the different INFN nodes who

collaborated on this project.

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