Original research: utilization of a convolutional neural network for automated detection of lytic spinal lesions on body CTs

Chang, Connie Y.; Huber, Florian A.; Yeh, Kaitlyn J.; Buckless, Colleen; Torriani, Martin

doi:10.1007/s00256-023-04283-x

Cited by 5 publications

(6 citation statements)

References 14 publications

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“…Our methodology instead does not require manual extractions. Authors in [36] consider a detection system for lytic spinal lesions. Differently from our approach, authors here consider only lytic lesions.…”

Section: A Related Workmentioning

confidence: 99%

“…To conclude, we perform a comparison with related works. In particular, we benchmark our results against the ones in [36] and [39]. The work in [36] proposes a detection scheme for lytic lesions using CNNs.…”

Section: E Comparison With Related Workmentioning

confidence: 99%

“…In particular, we benchmark our results against the ones in [36] and [39]. The work in [36] proposes a detection scheme for lytic lesions using CNNs. The work in [39] instead proposes a CADe for automatic detection and classification of sclerotic lesions.…”

Section: E Comparison With Related Workmentioning

confidence: 99%

“…Both the works use CT scans as input data for the system. We compare the Specificity, Recall and AUC metrics of our approach against the ones in [36], [39]. As for the Specificity, it is defined as…”

Section: E Comparison With Related Workmentioning

confidence: 99%

“…The aforementioned works do not include further details on other metrics, so we are not able to report them. Moreover, the work in [36] does not include the AUC, while the one in [39] does not include the Specificity. The results are provided in Table 7, where it can be noticed that our approach performs better in all the considered metrics.…”

Section: E Comparison With Related Workmentioning

confidence: 99%

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Automated Detection of Spinal Lesions From CT Scans via Deep Transfer Learning

Camisa,

Montanari,

Testa

et al. 2024

IEEE Access

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Convolutional Neural Networks are being increasingly applied to the detection of anomalies in Computed Tomographies (CTs). The goal of this paper is to implement an automated Computer-Aided Detection (CADe) system for spinal lesions using CTs and Convolutional Neural Networks pre-trained on commercial datasets. The proposed pipeline works as follows. The CADe takes in input CT scans and is equipped with an intuitive GUI to allow physicians to use it as a support tool for diagnoses. From the CT scans, the CADe selects volumes containing the vertebrae and extracts 2D slices of these volumes. These slices are pre-processed and then analyzed using a VGG19 Convolutional Neural Network and a tailored, binary classifier. The neural network identifies healthy vertebrae and vertebrae containing lesions (e.g. metastases, primary tumors, lytic and sclerotic lesions). For training and testing purposes, we generated a dataset from CTs of vertebrae from patients treated at IRCCS Rizzoli Orthopaedics Institute of Bologna, Italy, between 2009 and 2019. Both healthy and lesioned vertebrae retrieved with different tomography machines and setups are considered. The dataset has been enlarged using data augmentation techniques and subsequently used to train a wide range of deep learning models. We perform an in-depth benchmark study to assess the performance of the considered classifier against different Convolutional Neural Networks pretrained on the ImageNet dataset and exploiting Transfer Learning techniques. Leveraging Transfer Learning techniques, we reached 93.43% accuracy and a recall of 92.99%.

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Section: A Related Workmentioning

confidence: 99%

Section: E Comparison With Related Workmentioning

confidence: 99%

Section: E Comparison With Related Workmentioning

confidence: 99%

Section: E Comparison With Related Workmentioning

confidence: 99%

Section: E Comparison With Related Workmentioning

confidence: 99%

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Automated Detection of Spinal Lesions From CT Scans via Deep Transfer Learning

Camisa,

Montanari,

Testa

et al. 2024

IEEE Access

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Overcoming Imbalanced Data Challenges in Spinal Disorder Detection: An Integrated YOLOv5 and CNNs Approach with SMOTE

Nadda,

Boonchieng,

Liawrungrueang

2023

2023 27th International Computer Science and Engineering Conference (ICSEC)

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Machine Learning–Assisted Decision Making in Orthopaedic Oncology

Rizk,

Gonzalez,

Galoaa

et al. 2024

JBJS Reviews

Self Cite

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» Artificial intelligence is an umbrella term for computational calculations that are designed to mimic human intelligence and problem-solving capabilities, although in the future, this may become an incomplete definition. Machine learning (ML) encompasses the development of algorithms or predictive models that generate outputs without explicit instructions, assisting in clinical predictions based on large data sets. Deep learning is a subset of ML that utilizes layers of networks that use various inter-relational connections to define and generalize data.» ML algorithms can enhance radiomics techniques for improved image evaluation and diagnosis. While ML shows promise with the advent of radiomics, there are still obstacles to overcome.» Several calculators leveraging ML algorithms have been developed to predict survival in primary sarcomas and metastatic bone disease utilizing patient-specific data. While these models often report exceptionally accurate performance, it is crucial to evaluate their robustness using standardized guidelines.» While increased computing power suggests continuous improvement of ML algorithms, these advancements must be balanced against challenges such as diversifying data, addressing ethical concerns, and enhancing model interpretability.

show abstract

Original research: utilization of a convolutional neural network for automated detection of lytic spinal lesions on body CTs

Cited by 5 publications

References 14 publications

Automated Detection of Spinal Lesions From CT Scans via Deep Transfer Learning

Automated Detection of Spinal Lesions From CT Scans via Deep Transfer Learning

Overcoming Imbalanced Data Challenges in Spinal Disorder Detection: An Integrated YOLOv5 and CNNs Approach with SMOTE

Machine Learning–Assisted Decision Making in Orthopaedic Oncology

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