Artificial Intelligence

Tripathi, Satvik

doi:10.4018/978-1-7998-3499-1.ch001

Cited by 10 publications

(5 citation statements)

References 2 publications

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“…Artificial intelligence (AI) is revolutionizing the healthcare industry by offering innovative solutions to complex challenges. AI algorithms are programmed to analyze and interpret vast amounts of data, make informed decisions, and learn from their experiences [11]. In healthcare, AI is being leveraged to improve diagnosis, treatment, and patient care.…”

Section: Introductionmentioning

confidence: 99%

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Tripathi,

Tabari,

Mansur

et al. 2024

Diagnostics

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Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

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Section: Introductionmentioning

confidence: 99%

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Tripathi,

Tabari,

Mansur

et al. 2024

Diagnostics

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“…Convolutional neural networks have helped to significantly increase the accuracy of computer vision classification tasks in the last few years [30, 31, 32, 33, 34, 35]. These methods has also been used to identify multiple types of cancer, predict progression of tumor, and classify various types of skin diseases [36, 37, 38, 39, 40, 41].…”

Section: Introductionmentioning

confidence: 99%

HematoNet: Expert Level Classification of Bone Marrow Cytology Morphology in Hematological Malignancy with Deep Learning

Tripathi

Augustin

Sukumaran

et al. 2022

Preprint

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There have been few efforts made to automate the cytomorphological categorization of bone marrow cells. For bone marrow cell categorization, deep-learning algorithms have been limited to a small number of samples or disease classifications. In this paper, we proposed a pipeline to classify the bone marrow cells despite these limitations. Data augmentation was used throughout the data to resolve any class imbalances. Then, random transformations such as rotating between 0° to 90°, zooming in/out, flipping horizontally and/or vertically, and translating were performed. The model used in the pipeline was a CoAtNet and that was compared with two baseline models, EfficientNetV2 and ResNext50. We then analyzed the CoAtNet model using SmoothGrad and Grad-CAM, two recently developed algorithms that have been shown to meet the fundamental requirements for explainability methods. After evaluating all three models’ performance for each of the distinct morphological classes, the proposed CoAtNet model was able to outperform the EfficientNetV2 and ResNext50 models due to its attention network property that increased the learning curve for the algorithm which was represented using a precision-recall curve.

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“…Artificial Intelligence has taken a huge leap in the field of medicine, fostering more advanced diagnostic and prognostic outcomes. We have seen AI algorithms working in a completely clinical setting to detection of Covid using binary features [1, 2, 3, 4, 5]. The future of the ‘Standard’ medical practice will be here sooner than anticipated, where a patient will be seeing a computer before seeing a doctor through advances in artificial intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

RadGenNets: Deep Learning-Based Radiogenomics Model For Gene Mutation Prediction In Lung Cancer

Tripathi

Augustin

Moyer

et al. 2022

Preprint

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In this paper, we present our methodology that can use for predicting gene mutation in patients with non-small cell lung cancer (NSCLC). There are three major types of gene mutations that an NSCLC patient's gene structure can change epidermal growth factor receptor (EGFR), Kirsten rat sarcoma virus (KRAS), and Anaplastic lymphoma kinase (ALK). We worked with the clinical and genomics data for each patient as well CT scans. We preprocessed all of the data and then built a novel pipeline to integrate both the image and tabular data. We built a novel pipeline that used a fusion of Convolutional Neural Networks and Dense Neural Networks. Also, using a search approach, we pick an ensemble of deep learning models to classify the separate gene mutations. These models include EfficientNets, SENet, and ResNeXt WSL, among others. Our model achieved a high area under curve (AUC) score of 94% in detecting gene mutation.

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Artificial Intelligence

Cited by 10 publications

References 2 publications

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

HematoNet: Expert Level Classification of Bone Marrow Cytology Morphology in Hematological Malignancy with Deep Learning

RadGenNets: Deep Learning-Based Radiogenomics Model For Gene Mutation Prediction In Lung Cancer

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