The coming 15 years in gynaecological pathology: digitisation, artificial intelligence, and new technologies

Farnell, David; Huntsman, David; Bashashati, Ali

doi:10.1111/his.13991

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…As the health-related apps on mobile devices and smart personal trackers become popular, direct access to continuous realtime health information, such as temperature, heart rate, respiratory rate, electrocardiogram, body mass index, blood glucose, and blood oxygen content, can be recorded into individual health data. These data can then be incorporated into the EHR and laboratory information systems (LIS) [35] to reintegrate into a virtualized and digitalized person, which was not possible previously and was beyond what the human brain alone can accomplish [36] (Fig. 4).…”

Section: Pathologist-centered Medical Systemmentioning

confidence: 99%

Artificial intelligence and computational pathology

Cui

Zhang

2021

Laboratory Investigation

305

173

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Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception. The incorporation of scientific research through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, into clinical practice unlocks innovative approaches for patient care. Computational pathology is burgeoning subspecialty in pathology that promises a better-integrated solution to whole-slide images, multi-omics data, and clinical informatics. However, computational pathology faces several challenges, including the ability to integrate raw data from different sources, limitation of hardware processing capacity, and a lack of specific training programs, as well as issues on ethics and larger societal acceptable practices that are still solidifying. The establishment of the entire industry of computational pathology requires far-reaching changes of the three essential elements connecting patients and doctors: the local laboratory, the scan center, and the central cloud hub/portal for data processing and retrieval. Computational pathology, unlocked through information integration and advanced digital communication networks, has the potential to improve clinical workflow efficiency, diagnostic quality, and ultimately create personalized diagnosis and treatment plans for patients. This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of computational pathology.

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Section: Pathologist-centered Medical Systemmentioning

confidence: 99%

Artificial intelligence and computational pathology

Cui

Zhang

2021

Laboratory Investigation

305

173

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“…Qual será o papel da inteligência artificial diante da realidade de uso crescente de informação em meios eletrônicos e maior capacidade de auditamento, transparência e proteção de dados sigilosos? Em alguns setores da Oncologia, interface técnica e validação seguem rápida expansão, havendo áreas já muito desenvolvidas: Dermatologia e Patologia são grandes exemplos (Acs, Rantalainen, & Hartman, 2020;Barlow, 2020;Bera et al, 2019;Farnell, Huntsman, & Bashashati, 2020;Phillips et al, 2019;Putora et al, 2020;Shimizu & Nakayama, 2020;Tseng et al, 2020;Wang et al, 2019). Ainda mais se consideramos o avanço dos wearables (Kokts-Porietis et al, 2019;Lynch et al, 2019), que por meio de sensores já se mostram úteis na aferição e vigilância da atividade física, para citar o mínimo, e ao que tudo indica serão fonte semi-automática de inserção de dados dentro de sistemas (baseados em inteligência artificial inclusive), garantindo uma rede de informação cada vez mais e densa e, espera-se, eficaz no sentido da experiência de cuidado e melhores desfechos.…”

Section: Discussionunclassified

Telemonitorização de sintomas pós quimioterapia com dispositivos mobile: revisão integrativa da literatura

Moura

Inocêncio

Sant’Ana

et al. 2021

RSD

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O objetivo desse estudo foi avaliar se há benefícios no telemonitoramento de sintomas pós quimioterapia com dispositivos mobile em comparação com modelo tradicional de visitas médicas intervalares no cuidado de pacientes oncológicos, de modo a propiciar ganhos em desfechos prioritários para paciente. Trata-se de uma revisão integrativa de artigos publicados no Pubmed e Scielo nos últimos cinco anos. Termos de busca incluíram cancer, neoplasia, neoplasms, oncology, medical oncology, cancer care facilities, oncology service, telemedicine, telemonitoring, teleoncology, mobile health, mhealth, m-health, e-health, ehealth, videogame, mobile game, mobile app, app-technology, chemotherapy, drug therapy, treatment, drug therapy. Foram incluídos estudos randomizados e controlados, publicados em língua inglesa, pacientes oncológicos expostos a quimioterapia, supervisionados remotamente das toxidades agudas induzidas por quimioterapia, com os dados inseridos por dispositivos mobile. Para extração de dados, dois revisores utilizaram ficha com campos de informação padronizados. Os estudos analisados após critérios de elegibilidade apontam melhor controle de sintomas e desfechos. Conclui-se que telemonitorar toxidade pós quimioterapia por meio de dispositivos mobile gera melhor controle de sintomas e reduz complicações.

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“…Despite a relatively low cost, a single H&E slide contains so much information that a pathologist's diagnosis is the gold standard, or ground truth, for many disease processes. With the advent of modern pathology slide scanners as well as promising applications of artificial intelligence (AI) on digitized pathology slides [1][2][3][4], we believe that AI in the near future will play an important role in surgical pathology through providing diagnosis support tools and companion diagnostic tests.…”

Section: Introductionmentioning

confidence: 99%

The utility of color normalization for AI‐based diagnosis of hematoxylin and eosin‐stained pathology images

Boschman

Farahani

Darbandsari

et al. 2021

The Journal of Pathology

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The color variation of hematoxylin and eosin (H&E)-stained tissues has presented a challenge for applications of artificial intelligence (AI) in digital pathology. Many color normalization algorithms have been developed in recent years in order to reduce the color variation between H&E images. However, previous efforts in benchmarking these algorithms have produced conflicting results and none have sufficiently assessed the efficacy of the various color normalization methods for improving diagnostic performance of AI systems. In this study, we systematically investigated eight color normalization algorithms for AI-based classification of H&E-stained histopathology slides, in the context of using images both from one center and from multiple centers. Our results show that color normalization does not consistently improve classification performance when both training and testing data are from a single center. However, using four multi-center datasets of two cancer types (ovarian and pleural) and objective functions, we show that color normalization can significantly improve the classification accuracy of images from external datasets (ovarian cancer: 0.25 AUC increase, p = 1.6 e-05; pleural cancer: 0.21 AUC increase, p = 1.4 e-10). Furthermore, we introduce a novel augmentation strategy by mixing color-normalized images using three easily accessible algorithms that consistently improves the diagnosis of test images from external centers, even when the individual normalization methods had varied results. We anticipate our study to be a starting point for reliable use of color normalization to improve AI-based, digital pathology-empowered diagnosis of cancers sourced from multiple centers.

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The coming 15 years in gynaecological pathology: digitisation, artificial intelligence, and new technologies

Cited by 8 publications

References 27 publications

Artificial intelligence and computational pathology

Artificial intelligence and computational pathology

Telemonitorização de sintomas pós quimioterapia com dispositivos mobile: revisão integrativa da literatura

The utility of color normalization for AI‐based diagnosis of hematoxylin and eosin‐stained pathology images

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