Cancer diagnosis using artificial intelligence: a review

Shastry, K. Aditya; Sanjay, H. A.

doi:10.1007/s10462-021-10074-4

Cited by 17 publications

(6 citation statements)

References 124 publications

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“…Leveraging branches of AI may facilitate such research on survival. AI systems possess potential to assist in all stages of research to clinical care: from genomic alteration identification to even clinician-focused workflow tools ( 9 ).…”

Section: Introductionmentioning

confidence: 99%

Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review

Schreidah,

Gordon,

Adeuyan

et al. 2024

Front. Med.

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Skin cancer mortality rates continue to rise, and survival analysis is increasingly needed to understand who is at risk and what interventions improve outcomes. However, current statistical methods are limited by inability to synthesize multiple data types, such as patient genetics, clinical history, demographics, and pathology and reveal significant multimodal relationships through predictive algorithms. Advances in computing power and data science enabled the rise of artificial intelligence (AI), which synthesizes vast amounts of data and applies algorithms that enable personalized diagnostic approaches. Here, we analyze AI methods used in skin cancer survival analysis, focusing on supervised learning, unsupervised learning, deep learning, and natural language processing. We illustrate strengths and weaknesses of these approaches with examples. Our PubMed search yielded 14 publications meeting inclusion criteria for this scoping review. Most publications focused on melanoma, particularly histopathologic interpretation with deep learning. Such concentration on a single type of skin cancer amid increasing focus on deep learning highlight growing areas for innovation; however, it also demonstrates opportunity for additional analysis that addresses other types of cutaneous malignancies and expands the scope of prognostication to combine both genetic, histopathologic, and clinical data. Moreover, researchers may leverage multiple AI methods for enhanced benefit in analyses. Expanding AI to this arena may enable improved survival analysis, targeted treatments, and outcomes.

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Section: Introductionmentioning

confidence: 99%

Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review

Schreidah,

Gordon,

Adeuyan

et al. 2024

Front. Med.

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“…DL-based evalu ation has been widely applied to image classification and object detection [15] . D L is a machine learning technique based on artificial neural networks, and it h as achieved significant applications in many fields [16][17][18][19][20] . In medical image proce ssing, DL techniques have advantages such as high accuracy and strong adapta bility.…”

Section: Introductionmentioning

confidence: 99%

Multi-classification-assisted diagnosis of multi-scale lung adenocarcinoma pathological maps based on Scalenet101

Huang,

Li,

Yang

et al. 2023

Preprint

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Among all types of cancers, lung cancer is the deadliest cancer with a high mortality rate. Early diagnosis of lung cancer enables its timely and effective treatment, which can help reduce the risk of death. The aim of this research is to propose a deep learning approach for classifying and detecting lung adenocarcinoma tissues. The method was an improved Scalenet101 based on class activation mapping. (I) Scalenet101 classified lung adenocarcinoma tissue images into cancer, stromal and normal categories with the help of the Sigmoid. Then, the performance of Scalenet101 was validated using the pre-trained downstream techniques such as AlexNet, VGG16, VGG19, and ResNet50. (ii) Multi-scale fusion and weight fitting were performed to obtain multi-classification activation maps, which could improve the interpretability and accuracy in lung cancer evaluation. The performance of this model was tested using benchmark lung adenocarcinoma tissue images from WSSS4LUAD. The results showed that the model achieved an absolute classification accuracy of over 93.86% and an F1 score of over 95.44%.

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“…Precision cancer care is another example of the amalgamation of AI/ML algorithms and big cancer genomics data sets to identify or discover individual genetic or genomics diagnostic and/or prognostic factors, helping to charter personalized treatment plans [ 4 ]. The most successful application of AI in medicine is medical-imaging-based clinical diagnostic tools, where computer-vision approaches utilize the imaging data to obtain sensitive diagnostics [ 5 , 6 ]. Genetic data of the primary sequence type (DNA or RNA) have been explored rigorously with machine learning algorithms for the task of predicting/annotating various genomic elements, such as gene-structure prediction of intron splice sites, 3′ untranslated regions, promoters, and cis-regulatory elements [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

ChromoEnhancer: An Artificial-Intelligence-Based Tool to Enhance Neoplastic Karyograms as an Aid for Effective Analysis

Bokhari

Alhareeri

Aljouie

et al. 2022

Cells

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Cytogenetics laboratory tests are among the most important procedures for the diagnosis of genetic diseases, especially in the area of hematological malignancies. Manual chromosomal karyotyping methods are time consuming and labor intensive and, hence, expensive. Therefore, to alleviate the process of analysis, several attempts have been made to enhance karyograms. The current chromosomal image enhancement is based on classical image processing. This approach has its limitations, one of which is that it has a mandatory application to all chromosomes, where customized application to each chromosome is ideal. Moreover, each chromosome needs a different level of enhancement, depending on whether a given area is from the chromosome itself or it is just an artifact from staining. The analysis of poor-quality karyograms, which is a difficulty faced often in preparations from cancer samples, is time consuming and might result in missing the abnormality or difficulty in reporting the exact breakpoint within the chromosome. We developed ChromoEnhancer, a novel artificial-intelligence-based method to enhance neoplastic karyogram images. The method is based on Generative Adversarial Networks (GANs) with a data-centric approach. GANs are known for the conversion of one image domain to another. We used GANs to convert poor-quality karyograms into good-quality images. Our method of karyogram enhancement led to robust routine cytogenetic analysis and, therefore, to accurate detection of cryptic chromosomal abnormalities. To evaluate ChromoEnahancer, we randomly assigned a subset of the enhanced images and their corresponding original (unenhanced) images to two independent cytogeneticists to measure the karyogram quality and the elapsed time to complete the analysis, using four rating criteria, each scaled from 1 to 5. Furthermore, we compared the enhanced images with our method to the original ones, using quantitative measures (PSNR and SSIM metrics).

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Cancer diagnosis using artificial intelligence: a review

Cited by 17 publications

References 124 publications

Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review

Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review

Multi-classification-assisted diagnosis of multi-scale lung adenocarcinoma pathological maps based on Scalenet101

ChromoEnhancer: An Artificial-Intelligence-Based Tool to Enhance Neoplastic Karyograms as an Aid for Effective Analysis

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