Editorial for “A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology‐Radiology Fusion”

Ye, Zezhong

doi:10.1002/jmri.27630

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…As Zezhong Ye discusses in the editorial, the integration of deep learning in medical diagnostics requires not only sophisticated algorithms but also a deep understanding of the medical conditions being analyzed. The editorial emphasizes the importance of algorithmic complexity and the ability to interpret subtle diagnostic criteria, which seems to be lacking in the current AI models for scoliosis classification [39].…”

Section: Incorrect Classification Of Scoliosis By Ai Modelsmentioning

confidence: 99%

Artificial Intelligence in Scoliosis Classification: An Investigation of Language-Based Models

Fabijan,

Polis,

Fabijan

et al. 2023

JPM

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Open-source artificial intelligence models are finding free application in various industries, including computer science and medicine. Their clinical potential, especially in assisting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in AI for diagnostics, we conducted a study evaluating the abilities of AI models, including ChatGPT, Microsoft Bing, and Scholar AI, in classifying single-curve scoliosis based on radiological descriptions. Fifty-six posturographic images depicting single-curve scoliosis were selected and assessed by two independent neurosurgery specialists, who classified them as mild, moderate, or severe based on Cobb angles. Subsequently, descriptions were developed that accurately characterized the degree of spinal deformation, based on the measured values of Cobb angles. These descriptions were then provided to AI language models to assess their proficiency in diagnosing spinal pathologies. The artificial intelligence models conducted classification using the provided data. Our study also focused on identifying specific sources of information and criteria applied in their decision-making algorithms, aiming for a deeper understanding of the determinants influencing AI decision processes in scoliosis classification. The classification quality of the predictions was evaluated using performance evaluation metrics such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and balanced accuracy. Our study strongly supported our hypothesis, showing that among four AI models, ChatGPT 4 and Scholar AI Premium excelled in classifying single-curve scoliosis with perfect sensitivity and specificity. These models demonstrated unmatched rater concordance and excellent performance metrics. In comparing real and AI-generated scoliosis classifications, they showed impeccable precision in all posturographic images, indicating total accuracy (1.0, MAE = 0.0) and remarkable inter-rater agreement, with a perfect Fleiss’ Kappa score. This was consistent across scoliosis cases with a Cobb’s angle range of 11–92 degrees. Despite high accuracy in classification, each model used an incorrect angular range for the mild stage of scoliosis. Our findings highlight the immense potential of AI in analyzing medical data sets. However, the diversity in competencies of AI models indicates the need for their further development to more effectively meet specific needs in clinical practice.

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Section: Incorrect Classification Of Scoliosis By Ai Modelsmentioning

confidence: 99%

Artificial Intelligence in Scoliosis Classification: An Investigation of Language-Based Models

Fabijan,

Polis,

Fabijan

et al. 2023

JPM

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“…Ye [13] designed an AI-based method (called AI-biopsy) for the earlier diagnoses of prostate cancer through MRI labelled with histopathology data. The DL method is designed to differentiate 1) higher-risk tumors from lower-risk tumors and 2) benign from cancerous tumors.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Malibari¹,

Alshahrani²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

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Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases. Magnetic resonance imaging (MRI) is a widely utilized tool for the classification and detection of prostate cancer. Since the manual screening process of prostate cancer is difficult, automated diagnostic methods become essential. This study develops a novel Deep Learning based Prostate Cancer Classification (DTL-PSCC) model using MRI images. The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors. In addition, the fuzzy k-nearest neighbour (FKNN) model is utilized for classification process where the class labels are allotted to the input MRI images. Moreover, the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm (KHA) which results in improved classification performance. In order to demonstrate the good classification outcome of the DTL-PSCC technique, a wide range of simulations take place on benchmark MRI datasets. The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%.

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The Classification of the Prostate Cancer based on Transfer Learning Techniques

Khedr,

Wahed,

Al-Attar

et al. 2023

Preprint

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The most common cause of mortality worldwide and the most common male cancer is prostate cancer. According to the American Cancer Society. In the United States, there were 164,690 new instances of prostate cancer and at least 29,430 deaths from the disease in 2018, making up 9.5% of all new cancer cases. This will have a significant socioeconomic impact. Having the ability to determine the aggressiveness risk of confirmed prostate cancer could enhance the choice of proper treatment for individuals. This could lead to better outcomes, especially in terms of prostate cancer specific mortality. Deep learning-based significant prostate cancer classification has attracted a lot of attention because it may one day be used to support therapeutic decision-making. In this research we propose four models for classification the prostate cancer based on transfer learning algorithms (EfficentNet, DenseNet and Xception). We used two datasets for diagnosing prostate cancer. One of them is the standard dataset which consists of six grades of cancers and the other is a personal dataset from laboratory which is new dataset from treated patients at the hospital of Zagazig university. The results are obtained by using the standard dataset is approximately 93.6% accuracy using EfficientNetB7 pretrained model. The results of the created dataset are 97.08%, 97.98%, 98.87% by using EfiicientNet, DenseNet121 and Xception transfer learning models respectively. The results were compared with the state of arts, and it outperform all of them and also the model can be used in applications.

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Editorial for “A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology‐Radiology Fusion”

Cited by 3 publications

References 10 publications

Artificial Intelligence in Scoliosis Classification: An Investigation of Language-Based Models

Artificial Intelligence in Scoliosis Classification: An Investigation of Language-Based Models

Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

The Classification of the Prostate Cancer based on Transfer Learning Techniques

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