Application of Digital Imaging and Artificial Intelligence to Pathology of the Placenta

Marletta, Stefano; Pantanowitz, Liron; Santonicco, Nicola; Caputo, Alessandro; Bragantini, Emma; Brunelli, Matteo; Girolami, Ilaria; Eccher, Albino

doi:10.1177/10935266221137953

Cited by 9 publications

(3 citation statements)

References 61 publications

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“…AI development is progressing rapidly, with the introduction of several models that can perform various tasks such as the detection and the segmentation of various malignancies such as breast, pharynx, and thyroid carcinoma [27][28][29][30], and non-tumoral specimens, among others [31,32].…”

Section: Discussionmentioning

confidence: 99%

Value of Artificial Intelligence in Evaluating Lymph Node Metastases

Caldonazzi

Rizzo

Eccher

et al. 2023

Cancers

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One of the most relevant prognostic factors in cancer staging is the presence of lymph node (LN) metastasis. Evaluating lymph nodes for the presence of metastatic cancerous cells can be a lengthy, monotonous, and error-prone process. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic tissue. The aim of this study was to review the literature regarding the implementation of AI as a tool for the detection of metastases in LNs in WSIs. A systematic literature search was conducted in PubMed and Embase databases. Studies involving the application of AI techniques to automatically analyze LN status were included. Of 4584 retrieved articles, 23 were included. Relevant articles were labeled into three categories based upon the accuracy of AI in evaluating LNs. Published data overall indicate that the application of AI in detecting LN metastases is promising and can be proficiently employed in daily pathology practice.

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Section: Discussionmentioning

confidence: 99%

Value of Artificial Intelligence in Evaluating Lymph Node Metastases

Caldonazzi

Rizzo

Eccher

et al. 2023

Cancers

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“…Research on imaging of placental tissue sections dates back to 1897 [42], where a staining method is used for labeling. Very recent studies have reviewed the methods of imaging placental tissue, reflecting the importance of the topic [43][44][45]. Some of the July 19, 2023 9/31 prominent modalities used in placental imaging are ultrasound imaging [46,47] magnetic resonance imaging (MRI) [48][49][50], micro-computed tomography (micro-CT) [51,52], transmission electron microscopy and scanning electron microscopy [53][54][55][56],…”

Section: Discussionmentioning

confidence: 99%

Label-free differentiation of functional zones in mature mouse placenta using micro-Raman imaging

Inanc,

Bektas,

Kecoglu

et al. 2023

Preprint

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In histopathology, it is highly crucial to have chemical and structural information about tissues. Additionally, the segmentation of zones within a tissue plays an important role in investigating the functions of these regions for better diagnosis and treatment. The placenta plays an important role in embryonic and fetal development and in the diagnosis of some diseases associated with its dysfunction. This study provides a label-free approach to obtain the images of mature mouse placenta together with the chemical differences between the tissue compartments using Raman spectroscopy. To generate the Raman images, spectra of placental tissue were collected using a custom-built optical setup. The pre-processed spectra were analyzed using statistical and machine learning methods to acquire the Raman maps. We found that the placental regions called decidua and the labyrinth zone are biochemically distinct from the junctional zone. Comparison and evaluation of the Raman maps with histological images of the placental tissue were performed by a histologist and they are found to be in agreement. The results of this study show that Raman spectroscopy offers the possibility of label-free monitoring of the placental tissue from mature mice while revealing important structural information about the zones at the same time.

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“…These methods have the potential to be approved for use in clinical practice and often focus on common and impactful cancers such as breast and prostate cancer and melanocytic lesions. In parallel, AI can aid and augment pathologists in the evaluation of non-neoplastic diseases by helping in discrimination of entities, quantification of immunohistochemistry, and 3D reconstruction 7 , 51-53 .…”

Section: Session Highlightsmentioning

confidence: 99%

The slow-paced digital evolution of pathology: lights and shadows from a multifaceted board

Caputo¹,

L’Imperio

Merolla

et al. 2023

Pathologica

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Summary Objective The digital revolution in pathology represents an invaluable resource fto optimise costs, reduce the risk of error and improve patient care, even though it is still adopted in a minority of laboratories. Barriers include concerns about initial costs, lack of confidence in using whole slide images for primary diagnosis, and lack of guidance on transition. To address these challenges and develop a programme to facilitate the introduction of digital pathology (DP) in Italian pathology departments, a panel discussion was set up to identify the key points to be considered. Methods On 21 July 2022, an initial conference call was held on Zoom to identify the main issues to be discussed during the face-to-face meeting. The final summit was divided into four different sessions: (I) the definition of DP, (II) practical applications of DP, (III) the use of AI in DP, (IV) DP and education. Results Essential requirements for the implementation of DP are a fully tracked and automated workflow, selection of the appropriate scanner based on the specific needs of each department, and a strong commitment combined with coordinated teamwork (pathologists, technicians, biologists, IT service and industries). This could reduce human error, leading to the application of AI tools for diagnosis, prognosis and prediction. Open challenges are the lack of specific regulations for virtual slide storage and the optimal storage solution for large volumes of slides. Conclusion Teamwork is key to DP transition, including close collaboration with industry. This will ease the transition and help bridge the gap that currently exists between many labs and full digitisation. The ultimate goal is to improve patient care.

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Application of Digital Imaging and Artificial Intelligence to Pathology of the Placenta

Cited by 9 publications

References 61 publications

Value of Artificial Intelligence in Evaluating Lymph Node Metastases

Value of Artificial Intelligence in Evaluating Lymph Node Metastases

Label-free differentiation of functional zones in mature mouse placenta using micro-Raman imaging

The slow-paced digital evolution of pathology: lights and shadows from a multifaceted board

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