[The environmental impact of digital technology and artificial intelligence, in the time of digital pathology]. / Impacts environnementaux du numérique et de l'intelligence artificielle, à l'heure de la pathologie digitale.

While digitization and artificial intelligence represent the future of our specialty, future is also constrained by global warming and overstepping of planetary limits, threatening human health and the functioning of the healthcare system. The report by the Délégation ministérielle du numérique en santé and the French government's ecological planning of the healthcare system confirm the need to control the environmental impact of digital technology. Indeed, despite the promises of dematerialization, digital technology is a very material industry, generating greenhouse gas emissions, problematic consumption of water and mineral resources, and social impacts. The digital sector is impacting at every stage: (i) manufacture of equipment; (ii) use; and (iii) end-of-life of equipment, which, when recycled, can only be recycled to a very limited extent. This is a fast-growing sector, and the digitization of our specialty is part of its acceleration and its impact. Understanding the consequences of digitalization and artificial intelligence, and phenomena such as the rebound effect, is an essential prerequisite for the implementation of a sober, responsible, and sustainable digital pathology. The aim of this update is to help pathologists better understand the environmental impact of digital technology. As healthcare professionals, we have a responsibility to combine technological advances with an awareness of their impact, within a systemic vision of human health.

The carbon footprint of a surgical pathology laboratory in France.

OBJECTIVES: The health sector contributes to climate disruption through greenhouse gas (GHG) emissions. It accounts for 8% to 10% of France's GHG emissions. Although the medical community has been alerted to the problem, more data are needed. This study aimed to determine the carbon footprint of a surgical pathology laboratory. METHODS: The study was conducted in the surgical pathology laboratory at Saint Vincent hospital (Lille) in 2021. It represented 17,242 patient cases corresponding to 54,124 paraffin blocks. The 17 staff members performed cytology, immunohistochemistry, and in situ hybridization. The study included all inputs, capital equipment, freight, travel, energy consumption, and waste. Carbon emission factors were based on the French Agence De l'Environnement et de la Maîtrise de l'Energie database. RESULTS: In 2021, the pathology laboratory's carbon footprint was 117 tons of CO2 equivalent (t CO2e), corresponding to 0.5% of Saint Vincent hospital's total emissions. The most significant emissions categories were inputs (60 t CO2e; 51%), freight associated with inputs (24 t CO2e; 20%), and travel (14 t CO2e; 12%). Waste and energy generated 10 t CO2e (9%) and 9 t CO2e (8%), respectively. CONCLUSIONS: The pathology laboratory's carbon footprint was equivalent to the yearly carbon impact of 11 French inhabitants. This footprint is dominated by inputs and associated freight. This suggests an urgent need to develop ecodesign and self-sufficiency in our routine practices.

Interpretable HER2 scoring by evaluating clinical guidelines through a weakly supervised, constrained deep learning approach.

The evaluation of the Human Epidermal growth factor Receptor-2 (HER2) expression is an important prognostic biomarker for breast cancer treatment selection. However, HER2 scoring has notoriously high interobserver variability due to stain variations between centers and the need to estimate visually the staining intensity in specific percentages of tumor area. In this paper, focusing on the interpretability of HER2 scoring by a pathologist, we propose a semi-automatic, two-stage deep learning approach that directly evaluates the clinical HER2 guidelines defined by the American Society of Clinical Oncology/ College of American Pathologists (ASCO/CAP). In the first stage, we segment the invasive tumor over the user-indicated Region of Interest (ROI). Then, in the second stage, we classify the tumor tissue into four HER2 classes. For the classification stage, we use weakly supervised, constrained optimization to find a model that classifies cancerous patches such that the tumor surface percentage meets the guidelines specification of each HER2 class. We end the second stage by freezing the model and refining its output logits in a supervised way to all slide labels in the training set. To ensure the quality of our dataset's labels, we conducted a multi-pathologist HER2 scoring consensus. For the assessment of doubtful cases where no consensus was found, our model can help by interpreting its HER2 class percentages output. We achieve a performance of 0.78 in F1-score on the test set while keeping our model interpretable for the pathologist, hopefully contributing to interpretable AI models in digital pathology.

Can AI predict epithelial lesion categories via automated analysis of cervical biopsies: The TissueNet challenge?

The French Society of Pathology (SFP) organized its first data challenge in 2020 with the help of the Health Data Hub (HDH). The organization of this event first consisted of recruiting nearly 5000 cervical biopsy slides obtained from 20 pathology centers. After ensuring that patients did not refuse to include their slides in the project, the slides were anonymized, digitized, and annotated by expert pathologists, and finally uploaded to a data challenge platform for competitors from around the world. Competing teams had to develop algorithms that could distinguish 4 diagnostic classes in cervical epithelial lesions. Among the many submissions from competitors, the best algorithms achieved an overall score close to 95%. The final part of the competition lasted only 6 weeks, and the goal of SFP and HDH is now to allow for the collection to be published in open access for the scientific community. In this report, we have performed a "post-competition analysis" of the results. We first described the algorithmic pipelines of 3 top competitors. We then analyzed several difficult cases that even the top competitors could not predict correctly. A medical committee of several expert pathologists looked for possible explanations for these erroneous results by reviewing the images, and we present their findings here targeted for a large audience of pathologists and data scientists in the field of digital pathology.

A Partial Label-Based Machine Learning Approach For Cervical Whole-Slide Image Classification: The Winning TissueNet Solution.

Cervical cancer is the fourth most common cancer in women worldwide. To determine early treatment for patients, it is critical to accurately classify the cervical intraepithelial lesion status based on a microscopic biopsy. Lesion classification is a 4-class problem, with biopsies being designated as benign or increasingly malignant as class 1-3, with 3 being invasive cancer. Unfortunately, traditional biopsy analysis by a pathologist is time-consuming and subject to intra- and inter-observer variability. For this reason, it is of interest to develop automatic analysis pipelines to classify lesion status directly from a digitalized whole slide image (WSI). The recent TissueNet Challenge was organized to find the best automatic detection pipeline for this task, using a dataset of 1015 annotated WSI slides. In this work, we present our winning end-to-end solution for cervical slide classification composed of a two-step classification model: First, we classify individual slide patches using an ensemble CNN, followed by an SVM-based slide classification using statistical features of the aggregated patch-level predictions. Importantly, we present the key innovation of our approach, which is a novel partial label-based loss function that allows us to supplement the supervised WSI patch annotations with weakly supervised patches based on the WSI class. This led to us not requiring additional expert tissue annotation, while still reaching the winning score of 94.7%. Our approach is a step towards the clinical inclusion of automatic pipelines for cervical cancer treatment planning.Clinical relevance- The explanation of the winning Tis-sueNet AI algorithm for automated cervical cancer classification, which may provide insights for the next generation of computer assisted tools in digital pathology.