Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

Ong, Whee-Sze; Zhu, Lei; Tan, Yunlong; Teo, Ee Chin; Tan, Jiong Hao; Kumar, Naresh; Vellayappan, Balamurugan; Ooi, Beng Chin; Quek, Swee Tian; Makmur, Andrew; Hallinan, James Thomas Patrick Decourcy

doi:10.3390/cancers15061837

Cited by 11 publications

(16 citation statements)

References 182 publications

(178 reference statements)

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“…The advent of AI has irrevocably changed numerous fields, and healthcare is no exception [ 1 , 2 , 3 , 4 , 5 , 6 ]. AI—broadly defined as the ability of a computer or computer-controlled robot to perform tasks commonly associated with intelligent beings—has introduced a level of sophistication in data analysis and decision making that was previously unattainable.…”

Section: Historical Context and Evolutionmentioning

confidence: 99%

“…The management of spinal diseases is on the cusp of a transformative shift precipitated by the emergence and integration of artificial intelligence (AI) and machine learning (ML) into the realm of standard medical care [ 1 , 2 , 3 , 4 , 5 , 6 ]. Rather than being a vision of the distant future, this shift towards an intelligence-based spinal care model is well underway, promising a host of potential applications, including diagnosis, treatment, and the anticipation of adverse events [ 1 , 2 , 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…The advent of AI and ML in healthcare is not an isolated phenomenon but rather the logical outcome of decades of accumulated scientific and technological progress within computational and healthcare disciplines. AI and ML have transcended mere theoretical promise; they are already delivering tangible results in the present day [ 1 , 2 , 3 , 4 , 5 , 6 ]. One of the most striking examples of their efficacy lies in the realm of spinal imaging [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning

Yagi

Yamanouchi

Fujita

et al. 2023

JCM

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Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral components of modern healthcare, offering new avenues for diagnosis, treatment, and outcome prediction. This review explores their current applications and potential future in the field of spinal care. From enhancing imaging techniques to predicting patient outcomes, AI and ML are revolutionizing the way we approach spinal diseases. AI and ML have significantly improved spinal imaging by augmenting detection and classification capabilities, thereby boosting diagnostic accuracy. Predictive models have also been developed to guide treatment plans and foresee patient outcomes, driving a shift towards more personalized care. Looking towards the future, we envision AI and ML further ingraining themselves in spinal care with the development of algorithms capable of deciphering complex spinal pathologies to aid decision making. Despite the promise these technologies hold, their integration into clinical practice is not without challenges. Data quality, integration hurdles, data security, and ethical considerations are some of the key areas that need to be addressed for their successful and responsible implementation. In conclusion, AI and ML represent potent tools for transforming spinal care. Thoughtful and balanced integration of these technologies, guided by ethical considerations, can lead to significant advancements, ushering in an era of more personalized, effective, and efficient healthcare.

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Section: Historical Context and Evolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning

Yagi

Yamanouchi

Fujita

et al. 2023

JCM

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“…Artificial intelligence (AI) principles have enormous potential for a wide range of applications, including risk modeling and classification, self-detection, diagnostics, including the classification of small molecules into illness subgroups, and the prediction of treatment response and prognosis. AI is increasingly being used in medical and biological research as well as therapeutic treatment [ 14 , 15 ]. Several preclinical and clinical studies on healthcare have been conducted recently using supervised machine learning (SML) and various AI technologies.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Deep Learning Models for Cutaneous Leishmania Parasite Diagnosis Using Microscopic Images

Abdelmula,

Mirzaei,

Güler

et al. 2023

Diagnostics

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Cutaneous leishmaniasis (CL) is a common illness that causes skin lesions, principally ulcerations, on exposed regions of the body. Although neglected tropical diseases (NTDs) are typically found in tropical areas, they have recently become more common along Africa’s northern coast, particularly in Libya. The devastation of healthcare infrastructure during the 2011 war and the following conflicts, as well as governmental apathy, may be causal factors associated with this catastrophic event. The main objective of this study is to evaluate alternative diagnostic strategies for recognizing amastigotes of cutaneous leishmaniasis parasites at various stages using Convolutional Neural Networks (CNNs). The research is additionally aimed at testing different classification models employing a dataset of ultra-thin skin smear images of Leishmania parasite-infected people with cutaneous leishmaniasis. The pre-trained deep learning models including EfficientNetB0, DenseNet201, ResNet101, MobileNetv2, and Xception are used for the cutaneous leishmania parasite diagnosis task. To assess the models’ effectiveness, we employed a five-fold cross-validation approach to guarantee the consistency of the models’ outputs when applied to different portions of the full dataset. Following a thorough assessment and contrast of the various models, DenseNet-201 proved to be the most suitable choice. It attained a mean accuracy of 0.9914 along with outstanding results for sensitivity, specificity, positive predictive value, negative predictive value, F1-score, Matthew’s correlation coefficient, and Cohen’s Kappa coefficient. The DenseNet-201 model surpassed the other models based on a comprehensive evaluation of these key classification performance metrics.

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“…Meanwhile, Compared with traditional diagnosis, machine learning can train models with large amounts of data to improve the accuracy and precision of diagnosis and avoid the impact of doctors’ personal experience and subjective judgment on the diagnosis results. Machine learning can automatically analyze medical images, clinical features, and other information to quickly complete a large amount of work, reducing the workload of physicians and improving work efficiency ( 11 – 13 ). K. Zhao et al.…”

Section: Introductionmentioning

confidence: 99%

The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis

Li,

Dong,

Yuan

2023

Front. Oncol.

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BackgroundMalignant bone tumors are a type of cancer with varying malignancy and prognosis. Accurate diagnosis and classification are crucial for treatment and prognosis assessment. Machine learning has been introduced for early differential diagnosis of malignant bone tumors, but its performance is controversial. This systematic review and meta-analysis aims to explore the diagnostic value of machine learning for malignant bone tumors.MethodsPubMed, Embase, Cochrane Library, and Web of Science were searched for literature on machine learning in the differential diagnosis of malignant bone tumors up to October 31, 2022. The risk of bias assessment was conducted using QUADAS-2. A bivariate mixed-effects model was used for meta-analysis, with subgroup analyses by machine learning methods and modeling approaches.ResultsThe inclusion comprised 31 publications with 382,371 patients, including 141,315 with malignant bone tumors. Meta-analysis results showed machine learning sensitivity and specificity of 0.87 [95% CI: 0.81,0.91] and 0.91 [95% CI: 0.86,0.94] in the training set, and 0.83 [95% CI: 0.74,0.89] and 0.87 [95% CI: 0.79,0.92] in the validation set. Subgroup analysis revealed MRI-based radiomics was the most common approach, with sensitivity and specificity of 0.85 [95% CI: 0.74,0.91] and 0.87 [95% CI: 0.81,0.91] in the training set, and 0.79 [95% CI: 0.70,0.86] and 0.79 [95% CI: 0.70,0.86] in the validation set. Convolutional neural networks were the most common model type, with sensitivity and specificity of 0.86 [95% CI: 0.72,0.94] and 0.92 [95% CI: 0.82,0.97] in the training set, and 0.87 [95% CI: 0.51,0.98] and 0.87 [95% CI: 0.69,0.96] in the validation set.ConclusionMachine learning is mainly applied in radiomics for diagnosing malignant bone tumors, showing desirable diagnostic performance. Machine learning can be an early adjunctive diagnostic method but requires further research and validation to determine its practical efficiency and clinical application prospects.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42023387057.

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Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

Cited by 11 publications

References 182 publications

Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning

Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning

Assessment of Deep Learning Models for Cutaneous Leishmania Parasite Diagnosis Using Microscopic Images

The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis

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