Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification

Calisto, Francisco Maria; Santiago, Carlos; Nunes, Nuno; Nascimento, Jacinto C.

doi:10.1016/j.ijhcs.2021.102607

Cited by 66 publications

(34 citation statements)

References 60 publications

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“…These studies demonstrate that multimodality fusion features possess rich and complementary information that allows robust and highly accurate tumor characterization. Recently, Calisto et al (126) explored the value of multimodality imaging histology techniques in clinical applications and concluded that introducing the complementary diagnostic technique resulted in a significantly increased clinician productivity and improved diagnostic quality from a report on the behavior of 45 physicians from nine different institutions. 133) employed deep learning-based radiomics to assess treatment response in patients with esophageal squamous cell carcinoma directly from pretreatment CT images and indicated that the bestperforming ResNet50 model, superior to both radiomics and clinical models, could effectively and accurately forecast the response to neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma.…”

Section: Multimodality Analysismentioning

confidence: 99%

“…Recently, Calisto et al. ( 126 ) explored the value of multimodality imaging histology techniques in clinical applications and concluded that introducing the complementary diagnostic technique resulted in a significantly increased clinician productivity and improved diagnostic quality from a report on the behavior of 45 physicians from nine different institutions.…”

Section: Some Case Studies and Applicationsmentioning

confidence: 99%

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Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

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The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

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Section: Multimodality Analysismentioning

confidence: 99%

Section: Some Case Studies and Applicationsmentioning

confidence: 99%

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

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“…In recent years, AI has been developed, researched, and applied in many medical disciplines. Images are the most commonly used form of data for AI development, such as electrocardiograms or radiologic images [19][20][21]. Dermatopathology is particularly suited for deep learning algorithms, because pattern recognition in scanning magnification is fundamental for diagnosis [10,[22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence as a Diagnostic Decision Support Tool in Skin Disease: Protocol for an Observational Prospective Cohort Study

Escalé-Besa¹,

Fuster-Casanovas²,

Bröve³

et al. 2022

JMIR Res Protoc

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Background Dermatological conditions are a relevant health problem. Each person has an average of 1.6 skin diseases per year, and consultations for skin pathology represent 20% of the total annual visits to primary care and around 35% are referred to a dermatology specialist. Machine learning (ML) models can be a good tool to help primary care professionals, as it can analyze and optimize complex sets of data. In addition, ML models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and classification. Objective This study aims to perform a prospective validation of an image analysis ML model as a diagnostic decision support tool for the diagnosis of dermatological conditions. Methods In this prospective study, 100 consecutive patients who visit a participant general practitioner (GP) with a skin problem in central Catalonia were recruited. Data collection was planned to last 7 months. Anonymized pictures of skin diseases were taken and introduced to the ML model interface (capable of screening for 44 different skin diseases), which returned the top 5 diagnoses by probability. The same image was also sent as a teledermatology consultation following the current stablished workflow. The GP, ML model, and dermatologist’s assessments will be compared to calculate the precision, sensitivity, specificity, and accuracy of the ML model. The results will be represented globally and individually for each skin disease class using a confusion matrix and one-versus-all methodology. The time taken to make the diagnosis will also be taken into consideration. Results Patient recruitment began in June 2021 and lasted for 5 months. Currently, all patients have been recruited and the images have been shown to the GPs and dermatologists. The analysis of the results has already started. Conclusions This study will provide information about ML models’ effectiveness and limitations. External testing is essential for regulating these diagnostic systems to deploy ML models in a primary care practice setting.

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“…Artificial intelligence (AI), in which training data are used to develop a system, has become increasingly popular regarding clinical image analysis and disease diagnosis [8][9][10][11][12][13]. The US Food and Drug Administration has approved a device based on AI to diagnose DR, despite the fact that the application and development of AI in medicine are still in an infancy stage [14].…”

Section: Introductionmentioning

confidence: 99%

Emerging Trends and Research Foci in Artificial Intelligence for Retinal Diseases: Bibliometric and Visualization Study (Preprint)

Zhao¹,

Lü²,

Qian³

et al. 2022

Preprint

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BACKGROUND Patients with retinal diseases may exhibit serious complications that cause severe visual impairment owing to a lack of awareness of retinal diseases and limited medical resources. Understanding how artificial intelligence (AI) is used to make predictions and perform relevant analyses is a very active area of research on retinal diseases. In this study, the relevant Science Citation Index (SCI) literature on the AI of retinal diseases published from 2012 to 2021 was integrated and analyzed. OBJECTIVE The aim of this study was to gain insights into the overall application of AI technology to the research of retinal diseases from set time and space dimensions. METHODS Citation data downloaded from the Web of Science Core Collection database for AI in retinal disease publications from January 1, 2012, to December 31, 2021, were considered for this analysis. Information retrieval was analyzed using the online analysis platforms of literature metrology: Bibliometrc, CiteSpace V, and VOSviewer. RESULTS A total of 197 institutions from 86 countries contributed to relevant publications; China had the largest number and researchers from University College London had the highest H-index. The reference clusters of SCI papers were clustered into 12 categories. “Deep learning” was the cluster with the widest range of cocited references. The burst keywords represented the research frontiers in 2018-2021, which were “eye disease” and “enhancement.” CONCLUSIONS This study provides a systematic analysis method on the literature regarding AI in retinal diseases. Bibliometric analysis enabled obtaining results that were objective and comprehensive. In the future, high-quality retinal image–forming AI technology with strong stability and clinical applicability will continue to be encouraged.

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Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification

Cited by 66 publications

References 60 publications

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Using Artificial Intelligence as a Diagnostic Decision Support Tool in Skin Disease: Protocol for an Observational Prospective Cohort Study

Emerging Trends and Research Foci in Artificial Intelligence for Retinal Diseases: Bibliometric and Visualization Study (Preprint)

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