Artificial Intelligence (AI) as One Health Tool in Pathology to Monitor and Control Infectious Diseases

Dear Colleagues,

Artificial intelligence (AI) can be defined as the intelligence presented by some artificial entities, such as robots and computers. Over the last decade, AI has received much attention and is dramatically transforming all forms of human activities, including those in the medical field.

The first attempts to apply AI to medicine date back to the dawn of this new technological era, during the 1960s. However, more recently, the development of “deep learning” (DL) has driven and accelerated the use of AI in the framework of biomedical sciences, further widening its fields of application. This is clearly testified by the exponential growth of the scientific community’s commitment in this area of interest (https://pubmed.ncbi.nlm.nih.gov/?term=artificial+intelligence+AND+medicine&filter=years.2012-2022&timeline=expanded, accessed on 14 October 2022).

DL is a subset of machine learning and currently represents the state of the art in the field of visual object recognition (“computer vision”). In particular, the so-called “convolutional neural network” (CNN) is the most famous and effective DL model for image recognition tasks, which has been successfully applied to diagnostic imaging and pathology.

The effectiveness of these models relies on their ability to automatically infer hidden relations and patterns from a huge amount of data. Before the advent of these new technologies, indeed, it was the responsibility of the expert in the human domain to design the entire feature engineering pipeline; today, instead, these tools noticeably help in reducing that effort and in improving the overall performance of predictive models.

As far as pathology is concerned, most AI research is currently targeted toward use in neoplastic disorders, aiming to improve/standardize the diagnosis and grading of cancer. However, several research groups are currently developing CNNs to detect and score lesions caused by different pathogens. This is particularly true in veterinary medicine, as CNNs could act as efficient and effective tools to monitor the trend of infectious diseases and to detect severe threats to human health and/or livestock production early.

As a matter of fact, the slaughterhouse is widely recognized as a unique checkpoint that is used to assess the health status of farm animals, and it can suitably provide reliable information to the entire livestock industry. Consequently, several countries decided to adopt specific schemes to record inspection (i.e., pathological) data from slaughtered animals. Despite this, the overall size of the slaughtering industry, the impressive number of slaughtered animals per day in high-throughput abattoirs, as well as the high speed of the slaughter chain, make the systematic collection of data very challenging. Preliminary results indicate that CNN-based methods could be successfully applied to carry out such highly repetitive tasks, thus allowing farmers to properly monitor and control infectious diseases (e.g., enzootic pneumonia by Mycoplasma hyopneumoniae), which are distributed worldwide and strongly affect the use/abuse of antimicrobials. Likewise, CNNs could be trained to detect emerging, foreign, and zoonotic diseases early, which might represent a serious threat to human health, food safety, and the profitability of livestock farming.

Considering the recent occurrence and spread of severe human infectious diseases (e.g., COVID-19 and monkeypox virus infection), we do not rule out the concept that AI-based technologies could gain greater importance in human pathology in the near future.

Prof. Dr. Attilio Corradi
Prof. Dr. Giuseppe Marruchella
Dr. Angelo Porrello
Dr. Luca Ferrari
Prof. Dr. Anna Maria Cantoni
Guest Editors

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