A Diagnosis Support System for Veterinary Necropsy based on Bayesian

Networks

Vianney Sicard

, S

ebastien Assi

, La

etitia Dorso

, Florian Chocteau

and S

ebastien Picault

INRAE, Oniris, BIOEPAR, 44300, Nantes, France

Keywords:

Veterinarian Autopsy, Bayesian Network, Decision-support Tool, Cattle Diseases.

Abstract:

Veterinary autopsy requires a high level of expertise and skills that not all veterinarians necessarily master,

especially in the context of the desertiﬁcation of rural areas. The development of support systems is a chal-

lenging issue, since such a tool, to be considered relevant and accepted by practitioners in their diagnosis

process, must avoid any black box effect. The diagnosis support system we introduce here, IVAN (“Inno-

vative Veterinary Assisted Necropsy”), aims to engage the user in an explicit, understandable, validable and

reviewable process, able to cope with the speciﬁc issues of cattle necropsy. Besides, it provides uncertainty

management to deal with approximate lesion descriptions. IVAN relies on a Bayesian network to infer relevant

proposals at each step of the diagnostic process. IVAN was trained on a set of real autopsy cases from autopsy

reports, and its performance was assessed using another set of reports. In addition, the tool had to provide

results in short response time and be able to run the application on mobile device and web server. In addition

to demonstrating the feasibility of the approach, IVAN is a ﬁrst step towards other support systems in other

species and in broader contexts than autopsy.

1 INTRODUCTION

Autopsy (or necropsy) is the macroscopic morpholog-

ical examination of all organs, based on the dissection

of a corpse to either determine the cause of death, or

identify an ongoing pathological process that required

euthanasia. In cattle, autopsy is often necessary, when

sudden death of an animal or serial mortality occurs

in a farm, to provide the essential elements for a doc-

umented diagnosis and to set up the most appropri-

ate measures for the other animals in the herd, po-

tentially impacting public veterinary health. Veteri-

nary autopsy requires very speciﬁc skills (anatomy,

diagnostics, etc.) that not all ﬁeld veterinarians nec-

essarily master. The increasing scarcity of veterinar-

ians in rural areas makes this issue even more criti-

cal because of the potential severe sanitary and eco-

nomic impact of ﬂawed disease detection (even post-

mortem) in livestock. Connected intelligent medical

tools will provide a substantial assistance to ﬁeld vet-

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erinarians by guiding them throughout the diagnostic

process, proposing the most relevant organ to look at,

helping to identify potential diseases, and suggesting

complementary analyses.

The use of expert systems is a serious opportu-

nity to address the scarcity of veterinary practition-

ers with a specialization in necropsy. With the joint

development of artiﬁcial intelligence (AI), data avail-

ability and increased information technology (IT) re-

sources, it is possible to develop reliable systems to

model a realistic medical decision-making process in

order to assist non-expert veterinarians. However, the

user must be able to validate each step of the process,

hence, such a system must avoid a black box effect

by making explicit, unambiguous and understandable

proposals that can be assessed throughout the diag-

nostic process.

The transfer of veterinary autopsy expertise to

ﬁeld practitioners through a support system based

on AI is a real challenge. Expert systems dedi-

cated to diagnosis support have been developed in hu-

man medicine since the 60s, but they generally tar-

get a speciﬁc disease and require to integrate a large

amount of human knowledge. The speciﬁcity of au-

topsy is the multiplicity of possible diseases and of

intermediary steps (lesions, organs, morphological di-

Sicard, V., Assié, S., Dorso, L., Chocteau, F. and Picault, S.

A Diagnosis Support System for Veterinary Necropsy based on Bayesian Networks.

DOI: 10.5220/0010223106450654

In Proceedings of the 13th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2021) - Volume 2, pages 645-654

ISBN: 978-989-758-484-8

645

agnosis...), which leads to a highly combinatory pro-

cess. In this paper, we introduce IVAN (“Innova-

tive Veterinary Assisted Necropsy”), a system based

on Bayesian Networks (BN) with a compromise be-

tween veterinary expertise and data-based learning,

to provide the clearest possible process and leave the

choice and validation of each diagnostic step by the

veterinarian. To the best of our knowledge, there is

currently no similar solution in human nor veterinary

medicine which implied to develop new methods able

to address the speciﬁcities of necropsy.

The paper is structured as follows: ﬁrst, we

present related work and their limitation, together

with the Bayesian approach; second, we introduce

the principles and algorithms implemented in IVAN;

third, we present how we carried out the evaluation of

the system.

2 BACKGROUND

In this section, we provide an overview on existing

work on diagnosis support methods. We also brieﬂy

present the principles and interest of BN, and ﬁnally

emphasize the speciﬁcities of cattle necropsy.

2.1 Existing Diagnosis Support Methods

Existing medical support systems (e.g. Munin (An-

dreassen et al., 2001), Prostanet (Lacave and D

ıez,

2003)) focus on a few diseases (only one most of the

time) in living human beings, and generally lack ex-

plicit or understandable process. The ﬁrst (de Dombal

et al., 1972), developed in the 1960s, concerned only

heart disease and acute abdominal pain. They imple-

mented Bayes’ naive method and got good results on

simple issues. However, they were limited because

the observations were not always correlated. Sub-

sequently, more recent systems used uncertain rea-

soning (e.g. Munin), but, even if the diagnosis pro-

posals were very close to the expert’s, many incon-

sistencies suggested revising the underlying assump-

tions. Mycin (De Baets and Fodor, 1999) used BN

to solve both issues: uncertain reasoning and con-

sistency. Thereafter, softwares like Prostanet pro-

vided robust solutions with a very high level of exper-

tise. More recently, McKendrick (McKendrick et al.,

2000), Seidel (Seidel et al., 2003), Greenen (Geenen

et al., 2011) and Aristoteles (Aristoteles et al., 2019)

have conﬁrmed that expert systems based on BN are

relevant for diagnosis assistance, especially because

of its usage potential for use in a wide range of epi-

demiological disease situations. However, the solu-

tions mentioned above are applied to a single disease,

whereas the solution we propose in the context of vet-

erinary necropsy handles hundreds of diseases.

Other methods, based on deep learning methods,

particularly artiﬁcial neural network (Amato et al.,

2013), also focus on a single disease. In addition, they

do not enable explicit and understandable diagnostic

process (black box effect), which, currently, remains

a strong limitation to acceptability among veterinar-

ians. Hence, we preferred to rely upon a Bayesian

approach.

2.2 Bayesian Networks

A BN can be deﬁned as a probabilistic graphi-

cal model representing random variables (Ben-Gal,

2008). It is both a knowledge representation and

reasoning frame, a system for calculating conditional

probabilities, and the underlying architecture for de-

veloping an expert system.

A BN is a directed acyclic graph (DAG) composed

of sets of nodes connected by edges. Nodes represent

variables in the Bayesian sense: observable quanti-

ties, latent variables, unknown parameters or hypothe-

ses. Nodes and variables are equivalent (a node rep-

resents a single variable and a variable can be repre-

sented by only one node). The parameters describe

how each variable relates probabilistically to its par-

ents. Edges represent direct causal relationships be-

tween nodes.

The DAG of a BN necessarily respects the

Markov property, i.e. a node is independent of all

its non-descendent conditionally on its parents (Pearl,

2009) so we have a joint probability density (Pearl,

1982) (1).

P(X

, X

, ..., X

) =

∏

i=1

P(X

|π

)

Where π

is the parents of X.

(1)

Each node is conditionally independent of its non-

descendent.

Each node is endowed with its own conditional

probability table (CPT) (Figure 1) which gives the

probabilities of a variable with respect to the others.

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

646

Sprinkler

Grass wet

Rain

Sprinkler

T F

0.4 0.6

Rain

T F

0.2 0.8

Grass wet

Sprinkler Rain T F

F F 0.6 0.4

F T 0.87 0.13

T F 0.92 0.08

T T 0.99 0.01

Figure 1: Example of a trivial BN and associated CPT.

The marginal distribution for a given node with

children can be calculated applying the marginaliza-

tion algorithm (Pearl, 1982; Smail, 2004):

As an example, the calculation of the probability

to have grass wet true (P(G

) )

Example of calculation of P(G

) from the Fig. 1:

(G = Grass wet, S = Springler, R = Rain)

P(G

) = P(G

, R

) · P(S

) · P(R

)

+ P(G

, R

) · P(S

) · P(R

)

+ P(G

, R

) · P(S

) · P(R

)

+ P(G

, R

) · P(S

) · P(R

)

P(G

) = (0.6 ∗ 0.6 ∗ 0.8)

+ (0.87 ∗ 0.6 ∗ 0.2)

+ (0.92 ∗ 0.4 ∗ 0.8)

+ (0.99 ∗ 0.4 ∗ 0.2)

P(G

) = 0.766

(2)

The probability of non-observed data can be in-

ferred in the BN, following the direction opposite to

causal effect by applying Bayes’ theorem (BT) (3) or

its generalization to multiple nodes (4).

P(B|A) =

P(A|B) ·P(B)

P(A)

(3)











P(A|X

, X

, ..., X

) = α

∏

i=1

P(X

|A)

·P(A)

with α =

∑

i=1

P(A|X

)

(4)

where {X

, .., X

} is the vector of child nodes of A (all

assumed independent one from each other).

2.3 Animal Necropsy and Available

Data

The process of veterinary necropsy followed by spe-

cialized practitioners is composed of several inference

steps (Fig. 2). First, after recording general infor-

mation on the animal (sex, breed, age), the veteri-

narian decides to target several organs (step 1), on

which to search for lesions. Lesions consist in ab-

normal shape, size, color, consistency, content or dis-

tribution of either parts of organs or of whole organs,

each combination of these features resulting in a spe-

ciﬁc morphological diagnostic (MD). Then, the prac-

titioner deduces possible diagnoses from one or vari-

ous observed MD (step 2). Most of the time, making

the ﬁnal diagnosis is possible only after gaining ad-

ditional discriminant information (step 3), e.g. by se-

lecting other organs to look at, or performing biolog-

ical tests. The diversity of possible diseases and MD,

as well as the difﬁculty to identify proper MD from

observed lesions, are a key issue in animal necropsy

and rely both on a vast theoretical knowledge and on

daily experience.

animal information

Potential diseases

Target organs

STEP 1

What to focus on?

autopsy

Observed lesions

description

Morphological

diagnostics

Diagnoses

STEP 2

Which causes?

ﬁnal diagnosis

Additional tests

STEP 3

How to discriminate?

Figure 2: Main steps of the diagnostic process. Information

deduced by the practitioner can either stay implicit or be

mentioned explicitly in the ﬁnal necropsy report.

To model this process, two data sources were im-

mediately available. The ﬁrst one comes from four

years of autopsy reports performed on cattle by vet-

erinary necropsy specialists in a necropsy service in

a French veterinary school. The second is a cor-

pus of theoretical knowledge compiled by a veteri-

nary necropsy specialist concerning the description

of lesions, morphological diagnostic (MD) and dis-

A Diagnosis Support System for Veterinary Necropsy based on Bayesian Networks

647

eases. Recorded data is composed of epidemiological

and health information from reports for 783 cases of

bovine autopsies, involving 543 distinct MD, cover-

ing a total of 152 diseases. In each report, the cause

of death is attributed to a single disease based on

the observed MD. Theoretical knowledge is indeed

larger with a description of 379 diseases, involving

potentially 1914 MD (some of them being quite rare).

Hence, theoretical data can be used to complement

observations.

3 PRINCIPLES AND METHOD

In this section, we present the architecture and algo-

rithms used to implement our diagnostic support sys-

tem for veterinary necropsy (IVAN).

3.1 Intrinsic Complexity and Technical

Constraints

Using a classical BN structure (with each disease rep-

resented by a Boolean node) would involve 925 nodes

(i.e. 1850 Boolean parameters). Their combination

would lead to a huge computation time while the ﬁ-

nal application must be used on the ﬁeld and there-

fore must produce results in reasonable time. Since

autopsy reports conclude with a single disease, it was

possible for diseases to replace the multiple Boolean

nodes by a single node with multiple values. Besides,

several optimization methods were applied in the pro-

cessing of BN calculations.

The challenge was to address the following issues

simultaneously:

• no black box: the diagnosis process is not a

straight deduction from an initial set of input data;

it must instead be decomposed into several steps

which are all explicit, understandable, validable

and reviewable by the user and are expected to

guide the user and propose a prioritization of pos-

sible options

• uncertainty management: assist and guide non-

experts by providing them the most relevant pro-

posals for accurate input information

• reactivity: short response time even with highly

combinatorial process and mobile application

• versatility: the application must run on both a mo-

bile device and a web server

To solve versatility issues, we use the Meteor.js

framework which enables developing once in a single

language, here Javascript (JS), and then compile the

code for each platform. This comes with a limitation

due to JS, which is not a language dedicated to sci-

entiﬁc computing, and provides currently no speciﬁc

library for BN.

3.2 Calculation of Conditional

Probability Tables

To ensure that the diagnostic process followed by

IVAN mimics the usual diagnostic process as ex-

pected by the practitioner, the structure of the BN

was built with the help of veterinary necropsy spe-

cialists, not by learning the structure from the data.

The data provided by necropsy reports were restruc-

tured into tables (in xls format) directly by the vet-

erinary necropsy specialists, according to the speciﬁc

structure of the global BN (Na

ım et al., 2011). To cal-

culate each CPT corresponding to the different nodes

of the BN, we used the R library bnlearn (Scutari and

Denis, 2015) which provides outputs in its speciﬁc

format (.bif ).

Examples :

• description of the node age group deﬁned from

veterinary necropsy expertise:

variable age_group {

type discrete [ 5 ] {

ADULT, YOUNG, NEWBORN, CALF, OLD

};

}

• declaration of the CPT between the disease

Mannheimia (BR10) and the MD Pulmonary em-

physema (RP2430) calculate from data (the values

below give, on each line representing the presence

of MD RP2430, the probability of having or not

the disease BR10):

probability ( BR10 | RP2430 ) {

(present) 0.2403846, 0.7596154;

(absent) 0.01150121, 0.98849879;

}

To calculate CPT bnlearn needs the following inputs:

• the graph structure in a speciﬁc syntax,

e.g. for BN1: [sex][breed][age_group]

[diseases|sex:breed:age_group],

• the data table structure according to the BN struc-

ture,

• the method to use (here Bayesian method with an

imaginary sample size (iss) of 10 (Scutari and De-

nis, 2015)).

The output ﬁles generated by bnlearn are parsed

by the application to build the different BN needed.

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

648

3.3 Belief Propagation

Belief propagation in a BN can be calculated us-

ing either polytree propagation (PP) (Rebane and

Pearl, 1987) or junction tree (JT) (Madsen and Jensen,

1998). JT is usually used when the BN cannot be ex-

pressed as a polytree. In our case the BN is a polytree

(Fig. 3), so we used the PP algorithm.

To optimize calculation, marginal distributions are

computed “just in time” and results are stored for later

use. The propagation is calculated only for the branch

of the BN concerned with the Depth-First Search al-

gorithm. Thus, marginal distribution goes up to the

considered node and the path can be reused without

recalculation.

Belief propagation is based on the opposite effect

to causal effect. As mentioned above, generalised BT,

which involves only a single layer of the BN, limits

the number of calculations to perform. The compu-

tational cost of generalised BT is proportional to the

number of variables involved.

3.4 Subgraph Division

The formalisation of the diagnosis process is com-

posed of three main steps corresponding to the dif-

ferent stages of the veterinarian’s reﬂection process

to make a diagnosis (Fig. 4):

1. determine a set of potential diseases (and there-

fore their concerned organs to be autopsied) based

on animal information

2. diagnose diseases from a set of MD observed dur-

ing autopsy

3. propose additional tests to discriminate between

possible diseases

Each of these steps are conditioned by the choice

of the veterinarian from the list of proposals given

by the system. The respect of these validation break-

points is crucial to ensure that the system is not per-

ceived as a monolithic black box which would provide

ﬁnal outputs directly from inputs. On the contrary,

each step is seen as mimicking the corresponding rea-

soning step in the veterinary necropsy specialist’s di-

agnosis process.

It is therefore possible to divide the whole BN into

three subgraphs representing these steps. This signif-

icantly reduces the calculation times for BN process-

ing, making it possible to implement this method in

JS, hence enabling the ﬁnal application to be usable

in the ﬁeld.

To make the divisions, we had to adapt the struc-

ture of the BN with a speciﬁc approach consisting of

grouping nodes that have similar speciﬁcation (Smail

Global BN

breed

sex

age group

diseases

additionnal

tests

BN1

breed

sex

age group

diseases

BN2

diseases

BN3

diseases

additionnal

tests

Figure 3: Global architecture of the BN after node grouping,

and the different sub-graphs: BN1 → animal information to

diseases, BN2 → diseases to MD, BN3 → disease to addi-

tional tests. These subgraphs correspond to the successive

steps of the diagram process (Fig. 4.)

and Raoult, 2005), i.e. grouping each disease node

into a single one, compound of the parameters cor-

responding to each possible disease, and similarly for

additional tests. The disease node is used as a pivot

node for each sub-graph (Fig. 3).

For the ﬁrst division, the disease node contains the

parameters corresponding to each possible disease.

This node has three parent nodes corresponding to the

animal’s information (sex, age, breed). The second

division is the principal BN which produces the diag-

nosis. The disease node contains the parameters cor-

responding to each possible diseases. This node has

several children corresponding to the different possi-

ble MD. The third division is the simplest, with only

A Diagnosis Support System for Veterinary Necropsy based on Bayesian Networks

649

animal information

- d

Potential diseases

- o

Target organs

STEP 1

autopsy

description

- MD

MD found

- diag

Possible diagnoses

STEP 2

- add

Additional tests

STEP 3

Final diagnosis

veterinary procedure interaction with practician

system proposals inference step

Figure 4: Main steps of the diagnostic process in IVAN.

The veterinarian chooses whether to acknowledge or revise

proposals at each key step identiﬁed previously.

two nodes connecting diseases and additional tests.

At this stage, the practitioner can select the dis-

eases which seems the more probable to get a pro-

posal of additional test for further discrimination.

3.5 Clustering of Subgraph Inferences

To facilitate the interpretation of outputs inferred in

each subgraph (hence user’s subsequent choice), the

proposals are not displayed directly with their cal-

culated probability value, which is expected mainly

to provide a conﬁdence level and a relative order

of proposals. Thus, results are clustered into 1 to

3 categories (High, Medium and Low probabilities)

without changing their order (Fig. 5), using the K-

means algorithm (Lloyd, 1982) with Euclidean dis-

tance. The relevant number of classes is determined

by the Silhouettes method (Rousseeuw, 1987), to en-

sure that e.g. three medium-probability outputs are

gathered into the “Medium probability” class rather

than dispatched into High, Medium and Low proba-

bility classes respectively.

This approach is a way to cope with the uncer-

tainty of observations or descriptions, which results

in the relatively low relevance of providing the user

with absolute probability values. According to veteri-

nary necropsy specialists’ evaluation of the tool, this

qualitative classiﬁcation was much more meaningful

than a list sorted by probability value.

Figure 5: Screenshot of IVAN proposing diagnoses (cen-

ter, left) grouped by probability levels (center, right) rang-

ing from high to low, based on observed MD (upper right

corner).

3.6 Morphological Diagnosis

Discrimination

To perform the diagnostic process with the BN, the

veterinarian must enter the appropriate MD name into

the system. This can be an issue if the veterinarian

is not familiar with MD identiﬁcation. To solve this

problem, MD can be inferred from the description

of organ lesions, even imprecise. Each MD was de-

scribed by veterinary necropsy specialists according

to the following criteria: size, shape, colour, consis-

tency, content and distribution. In order to manage the

uncertainty of descriptions, veterinary necropsy spe-

cialists have established a list of valid values for each

criterion and determined a distance between those

values to qualify their differences.

Each criterion k is associated with a set of n

valid

values a

(e.g. k = colour =⇒ a

= white) with

a distance measure between each pair of valid val-

ues (|a

− a

|) deﬁned by veterinary necropsy spe-

cialists. The theoretical description of a lesion on

an organ, associated to a speciﬁc MD, is a vector :

∗

= (a

∗k

)

i=1,n

with k = {shape, size, color, consis-

tency, content, distribution} and (a

∗k

)

i=1,n

being the

set of valid values for criterion k. The description of

an observed lesion, entered by the veterinary during

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

650

Figure 6: Example of the description of distances between

parameters for the shape of the lesion.

the diagnosis process, is a vector V = (a

)

i=1,n

. The

distance matrix D = (|a

∗k

− a

i, j

allows to identify

the most relevant MD associated to the lowest value

∑

. (Fig. ??), proposed for validation to the

user.

3.7 Supervised Learning

IVAN is enriched by supervised learning based on its

usage. The results of each diagnostic step are stored in

the database in separate table. These records are peri-

odically validated or discarded by veterinary necropsy

specialists and then re-injected into the process of the

calculation of conditional probability tables to update

them. This process is semi-automated, the valida-

tion step by a veterinary necropsy specialist being the

only section executed by humans. For now, this step

is mandatory to ensure that user-generated informa-

tion is reliable enough (especially because IVAN user

keeps the full responsibility for the ﬁnal diagnosis).

4 TESTS AND VALIDATION

4.1 Execution Time

The use of the application on the ﬁeld requires low re-

sponse times. We have tested it on a server with 2 GB

RAM and 2 CPUs. The test of performance was done

on the different steps separately to determine which

one takes the more execution time (Table 1).

As expected, the more MD there are, the longer

the execution time of the second step is, but this re-

Table 1: Typical execution time for each BN corresponding

to the three main steps. The crucial step (from MD to dis-

ease) was assessed with both a simple case (1 MD) and the

most complex case found on reports (6 MD).

BN1 BN2 BN3

(organs) 1 MD 6 MD

< 2s < 1s < 15s < 1s

mains within an acceptable time range for real-world

use, compared to the time spent by the veterinary for

necropsic exams (about 1h for an adult cow).

4.2 Protocol for Assessing Diagnosis

Proposals

To carry out a preliminary evaluation of IVAN, we

used a set of 40 new autopsy reports that were not in-

cluded in the set of learning reports. We considered

two criteria: a score with respect to the best proposal,

and the relevance of the diagnostic. Both can be as-

sessed for each step of the process.

The score refers to the relative position (based on

the rank from 1 to number of proposals nbProp) of a

real diagnosis item (organ, MD or disease mentioned

in the autopsy report) in the list of IVAN proposals for

the corresponding step, ordered by decreasing proba-

bility (5). A score between 1 and 0.5 means that the

diagnosis was found in the ﬁrst half of the list pro-

posed by the software.

(

score = 0 if real diagnosis not proposed

score =

nbProp−rank+1

nbProp

otherwise

(5)

The relevance refers to the presence or absence

of each diagnosis in the list of results, which means

that IVAN’s proposals are considered relevant if one

of them matches the real-case diagnosis as mentioned

in the ﬁnal report. This measure was deﬁned in accor-

dance with veterinary necropsy specialists’ point of

view, to account for the lack of explicit intermediary

hypotheses, possibly formulated during the necropsic

process, but discarded from the ﬁnal report. Indeed,

when used by a non-expert veterinarian in real situ-

ations, the correct diagnosis may have a poor score,

because alternative diagnoses that are highly proba-

ble without prior information may be discarded later

due to additional exams. Thus, the relevance is a way

to assess whether IVAN is able to propose the right

diagnosis among its proposals.

A Diagnosis Support System for Veterinary Necropsy based on Bayesian Networks

651

Figure 7: Average score and relevance of IVAN on a sample

of 40 new reports.

4.3 Results

Figure 7 shows that most of the results are in ﬁrst half

of the list for all subgraphs outputs. The low score for

the ﬁrst step (0.46) is due to the wide range of possi-

bilities at this stage of the diagnosis, but in terms of

performance, it can be considered that the application

is able to provide sound advice. In contrast, the third

step has very high score. This is due to the limited

range of additional tests.

The real interest lies in the second step, which cor-

responds to the diagnostic step. Most of the results are

at the top of the ﬁrst half of the list (0.79) and provide

the correct diagnosis in 78% of cases. The result is in

the high average of a veterinarian practitioner who is

not a specialist in cattle necropsy. We can probably

achieve better results with a larger learning sample.

5 DISCUSSION AND

PERSPECTIVES

To the best or our knowledge IVAN is the ﬁrst system

to support the diagnosis of veterinary necropsy based

on Bayesian Networks.

Necropsic diagnosis requires a high level of ex-

pertise, which is not necessarily mastered by ﬁeld vet-

erinarians. IVAN is a helpful assistant for practition-

ers by providing relevant proposals at each step of the

necropsic procedure without any black box effect and

leaving the ﬁnal decision to the veterinarian. The di-

agnosis process of the system is fully understandable,

validable and reviewable by the user, including un-

certainty management by guiding the veterinarian in

describing the lesion to give the system the exact mor-

phological diagnosis required.

The ﬁrst performance tests are encouraging, both

in terms of execution time and relevance of the diag-

nostic proposals. According to veterinary necropsy

specialists, who are also lecturers at a public veteri-

nary school, IVAN results would be higher in average

than students results in ﬁnal year of veterinary school,

and at least equal or better than ﬁeld veterinarians not

expert but familiar with autopsies.

5.1 Further Assessment

The purpose of a tool like IVAN is to enhance the

reliability of the necropsies performed by ﬁeld vet-

erinarians, especially in the context of an increased

scarcity of veterinary necropsy specialists. Thus, the

tool should either make non-experts more conﬁdent in

their diagnosis, or come to a diagnosis at all.

Thus, to evaluate IVAN in more realistic condi-

tions as an intelligent assistant, we need to compare

the outcomes of an interaction between a non-expert

practitioner and the software throughout a necropsy,

with two extremes: on the one hand, the conclusions

of the veterinary necropsy specialist (used as a gold

standard), and on the other hand, the conclusions of

the non-expert, assumed incorrect in many cases (ac-

cording to veterinary necropsy specialists).

We plan to do so in the forthcoming months with

the help of ﬁeld veterinarians or of veterinary thesis

students engaged in a specialization in necropsy. For

each step of the necropsic process, proposals made by

the non-expert practitioner and by IVAN will be as-

sessed by the veterinary necropsy specialist (in terms

of score and relevance), then the non-expert prac-

titioner will be provided with IVAN proposals and

asked either to keep its conclusions or revise them,

which will lead to a third assessment by the veteri-

nary necropsy specialist (Fig. 8).

Thus, we will be able to evaluate to what extent

the conclusions of the non-expert veterinarians may

either be comforted by IVAN proposals, or enhanced

by its suggestions, as a measure of the quality of the

support provided by the machine. The study of how

frequently erroneous conclusions of a non-expert are

modiﬁed when confronted to correct conclusions by

the software will also provide insights on the accept-

ability of the software as an “intelligent assistant”.

Conversely, cases where IVAN proposals are incor-

rect will highlight cases underrepresented in learning

data.

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Necropsy expert

observation

assessment1

assessment2

assessment3

Nurse

Vet student/Field vet

observation

proposals1

proposals3

(aer reading proposals2)

IVAN

submission

proposals2

Figure 8: Evaluation protocol to be carried out for each

of the three steps of the diagnostic process, to assess the

added-value of IVAN for assisting a non-expert veterinar-

ian during on-ﬁeld necropsy.

5.2 Perspectives

In addition to the intended experimental assessment,

IVAN will be integrated in the coming months within

a professional software platform to be proposed as an

e-service to ﬁeld veterinarians. This will be an op-

portunity to carry out a thorough assessment of the

tool usage and provide data from real use cases. A

procedure will be set up to assess the reliability of

necropsies carried out with IVAN and integrate vali-

dated reports into the learning database.

The ﬁrst results of IVAN on bovine necropsy make

it possible, ﬁrstly, to consider other breeds and in a

further step, to apply the process on living animals,

including clinical signs instead of lesion description.

Supporting necropsy for other breeds can rely upon

the autopsy reports for bovine. That can be applied

quite readily and quickly in the continuity of the cur-

rent work. The post-mortem diagnosis can also ben-

eﬁt from knowledge regarding the ante mortem con-

dition. The issues to address to do so are similar for

the diagnosis of living animals, since they rely on the

same kind of hypothetico-deductive inferences. This

will be the major challenge for the next stages of

IVAN, and for the development of a speciﬁc tool to

support living animal diagnosis.

6 CONCLUSION

We consider our contribution a ﬁrst step towards the

development of diagnosis support systems for veteri-

nary necropsy, able to assist non-expert practitioners

with relevant, reviewable, step-by-step recommenda-

tions mixing recorded cases and theoretical knowl-

edge. IVAN’s database will be enriched by, on the one

hand, new necropsy reports performed at the autopsy

service, and, on the other hand, on-ﬁeld records stored

in IVAN performed by non-expert practitioners, after

validation by veterinary necropsy specialists.

Though initiated in the context of bovine

necropsy, our approach is generic enough to apply

to other species and to living animals. The choice

of a Bayesian Network for learning and probabilistic

inference enables to cope with uncertainties encoun-

tered in the descriptions of lesions and resulting from

the scarcity of data, but also to provide a natural de-

composition of the diagnostic process, mimicking the

inferences made by a veterinary necropsy specialist,

hence facilitating the acceptance of the system by the

user. Such systems could be an opportunity for AI to

provide a substantial assistance to ﬁeld veterinarians

in the context of the medical desertiﬁcation of rural

areas.

ACKNOWLEDGEMENTS

This work is being carried out as part of the

Telemedecine Business Chair, co-ﬁnanced by MSD

Animal Health and Oniris - Veterinary Medicine

School of Nantes.

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