Photoacoustic Imaging for Cancer Diagnosis: A Breast Tumor Example

Gharieb, R.R.

doi:10.5772/intechopen.92084

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…This can further aid in the early diagnosis of diseases in asymptomatic cases monitoring the response to treatments. , Although the spectral data variation in progressive tumor conditions detected by the PAS technique is unique, these are not always identifiable. These necessitate the application of machine learning models to identify the spectral data for its improved efficacy, interpretability, and generalization ability . Machine learning algorithms such as SVM and mRMR can aid in classifying PA spectra accurately and efficiently, enabling a reliable diagnosis of diseases and therapy responses.…”

Section: Discussionmentioning

confidence: 99%

“…These necessitate the application of machine learning models to identify the spectral data for its improved efficacy, interpretability, and generalization ability. 68 Machine learning algorithms such as SVM and mRMR can aid in classifying PA spectra accurately and efficiently, enabling a reliable diagnosis of diseases and therapy responses.…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning Enabled Photoacoustic Spectroscopy for Noninvasive Assessment of Breast Tumor ProgressionIn Vivo: A Preclinical Study

Rodrigues,

Amin,

Chandra

et al. 2024

ACS Sens.

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Breast cancer is a dreaded disease affecting women the most in cancer-related deaths over other cancers. However, early diagnosis of the disease can help increase survival rates. The existing breast cancer diagnosis tools do not support the early diagnosis of the disease. Therefore, there is a great need to develop early diagnostic tools for this cancer. Photoacoustic spectroscopy (PAS), being very sensitive to biochemical changes, can be relied upon for its application in detecting breast tumors in vivo. With this motivation, in the current study, an aseptic chamber integrated photoacoustic (PA) probe was designed and developed to monitor breast tumor progression in vivo, established in nude mice. The device served the dual purpose of transporting tumor-bearing animals to the laboratory from the animal house and performing PA experiments in the same chamber, maintaining sterility. In the current study, breast tumor was induced in the nude mice by MCF-7 cells injection and the corresponding PA spectra at different time points (day 0, 5, 10, 15, and 20) of tumor progression in vivo in the same animals. The recorded photoacoustic spectra were subsequently preprocessed, wavelet-transformed, and subjected to filterbased feature selection algorithm. The selected top 20 features, by minimum redundancy maximum relevance (mRMR) algorithm, were then used to build an input feature matrix for machine learning (ML)-based classification of the data. The performance of classification models demonstrated 100% specificity, whereas the sensitivity of 95, 100, 92.5, and 85% for the time points, day 5, 10, 15, and 20, respectively. These results suggest the potential of PA signal-based classification of breast tumor progression in a preclinical model. The PA signal contains information on the biochemical changes associated with disease progression, emphasizing its translational strength toward early disease diagnosis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine Learning Enabled Photoacoustic Spectroscopy for Noninvasive Assessment of Breast Tumor ProgressionIn Vivo: A Preclinical Study

Rodrigues,

Amin,

Chandra

et al. 2024

ACS Sens.

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“…Combines optical and ultrasound imaging for high-resolution images Diagnosing cancer [198,199] and brain diseases [200] Smart clothing (Time series)…”

Section: Measures the Electrical Activity Of Sweat Glandsmentioning

confidence: 99%

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Chato,

Regentova

2023

JPM

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Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

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A modified source-detector configuration for the discrimination between normal and diseased human breast based on the continuous-wave diffuse optical imaging approach: a simulation study

2021

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Photoacoustic Imaging for Cancer Diagnosis: A Breast Tumor Example

Cited by 3 publications

References 26 publications

Machine Learning Enabled Photoacoustic Spectroscopy for Noninvasive Assessment of Breast Tumor ProgressionIn Vivo: A Preclinical Study

Machine Learning Enabled Photoacoustic Spectroscopy for Noninvasive Assessment of Breast Tumor ProgressionIn Vivo: A Preclinical Study

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

A modified source-detector configuration for the discrimination between normal and diseased human breast based on the continuous-wave diffuse optical imaging approach: a simulation study

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