FuseMedML: a framework for accelerated discovery in
machine learning based biomedicine

Golts, Alex; Raboh, Moshe; Shoshan, Yoel; Polaczek, Sagi; Rabinovici-Cohen, Simona; Hexter, Efrat

doi:10.21105/joss.04943

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Subsequently, in the corresponding patient-based method, a patient’s score was determined as the mean of all the image scores associated with that patient. To facilitate the rapid exploration of different methods, as well as the evaluation and comparison of these methods, we utilized the FuseMedML open source [ 16 ] in some instances. Additional details on FuseMedML can be found in Appendix B .…”

Section: Methodsmentioning

confidence: 99%

From Pixels to Diagnosis: Algorithmic Analysis of Clinical Oral Photos for Early Detection of Oral Squamous Cell Carcinoma

Rabinovici-Cohen,

Fridman,

Weinbaum

et al. 2024

Cancers

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Oral squamous cell carcinoma (OSCC) accounts for more than 90% of oral malignancies. Despite numerous advancements in understanding its biology, the mean five-year survival rate of OSCC is still very poor at about 50%, with even lower rates when the disease is detected at later stages. We investigate the use of clinical photographic images taken by common smartphones for the automated detection of OSCC cases and for the identification of suspicious cases mimicking cancer that require an urgent biopsy. We perform a retrospective study on a cohort of 1470 patients drawn from both hospital records and online academic sources. We examine various deep learning methods for the early detection of OSCC cases as well as for the detection of suspicious cases. Our results demonstrate the efficacy of these methods in both tasks, providing a comprehensive understanding of the patient’s condition. When evaluated on holdout data, the model to predict OSCC achieved an AUC of 0.96 (CI: 0.91, 0.98), with a sensitivity of 0.91 and specificity of 0.81. When the data are stratified based on lesion location, we find that our models can provide enhanced accuracy (AUC 1.00) in differentiating specific groups of patients that have lesions in the lingual mucosa, floor of mouth, or posterior tongue. These results underscore the potential of leveraging clinical photos for the timely and accurate identification of OSCC.

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Section: Methodsmentioning

confidence: 99%

From Pixels to Diagnosis: Algorithmic Analysis of Clinical Oral Photos for Early Detection of Oral Squamous Cell Carcinoma

Rabinovici-Cohen,

Fridman,

Weinbaum

et al. 2024

Cancers

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“…We used the Python scikit-learn 0.24.2 package ( 30 ) to construct a predictive machine learning model. MLP can be developed with the open source FuseMedML ( 31 ), a PyTorch-based deep learning framework for medical data.…”

Section: Methodsmentioning

confidence: 99%

“…Also, we validated the results of the ensemble model for the sub-cohorts corresponding to low (1-2) and high (>=3) number of treatment lines that might be associated with survival. The FuseMedML ( 31 ) open source package was used to perform all the above-mentioned evaluation methods and tests.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence-based prediction of overall survival in metastatic renal cell carcinoma

Barkan

Porta²,

Rabinovici-Cohen³

et al. 2023

Front. Oncol.

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Background and objectivesInvestigations of the prognosis are vital for better patient management and decision-making in patients with advanced metastatic renal cell carcinoma (mRCC). The purpose of this study is to evaluate the capacity of emerging Artificial Intelligence (AI) technologies to predict three- and five-year overall survival (OS) for mRCC patients starting their first-line of systemic treatment.Patients and methodsThe retrospective study included 322 Italian patients with mRCC who underwent systemic treatment between 2004 and 2019. Statistical analysis included the univariate and multivariate Cox proportional-hazard model and the Kaplan-Meier analysis for the prognostic factors’ investigation. The patients were split into a training cohort to establish the predictive models and a hold-out cohort to validate the results. The models were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We assessed the clinical benefit of the models using decision curve analysis (DCA). Then, the proposed AI models were compared with well-known pre-existing prognostic systemsResultsThe median age of patients in the study was 56.7 years at RCC diagnosis and 78% of participants were male. The median survival time from the start of systemic treatment was 29.2 months; 95% of the patients died during the follow-up that finished by the end of 2019. The proposed predictive model, which was constructed as an ensemble of three individual predictive models, outperformed all well-known prognostic models to which it was compared. It also demonstrated better usability in supporting clinical decisions for 3- and 5-year OS. The model achieved (0.786 and 0.771) AUC and (0.675 and 0.558) specificity at sensitivity 0.90 for 3 and 5 years, respectively. We also applied explainability methods to identify the important clinical features that were found to be partially matched with the prognostic factors identified in the Kaplan-Meier and Cox analyses.ConclusionsOur AI models provide best predictive accuracy and clinical net benefits over well-known prognostic models. As a result, they can potentially be used in clinical practice for providing better management for mRCC patients starting their first-line of systemic treatment. Larger studies would be needed to validate the developed model

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“…The fuse-med-ml [16] (version 0.1.10, open-source Python) library was used throughout the image classification process. Given the binary labels, the ensuing classification task was attempted by three different model types, using the pre-processed OCT images or the clinical features as input.…”

Section: Model Pipelinementioning

confidence: 99%

A Multi-Modal AI-Driven Cohort Selection Tool to Predict Suboptimal Non-Responders to Aflibercept Loading-Phase for Neovascular Age-Related Macular Degeneration: PRECISE Study Report 1

Chorev

Haderlein

Chandra

et al. 2023

JCM

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Patients diagnosed with exudative neovascular age-related macular degeneration are commonly treated with anti-vascular endothelial growth factor (anti-VEGF) agents. However, response to treatment is heterogeneous, without a clinical explanation. Predicting suboptimal response at baseline will enable more efficient clinical trial designs for novel, future interventions and facilitate individualised therapies. In this multicentre study, we trained a multi-modal artificial intelligence (AI) system to identify suboptimal responders to the loading-phase of the anti-VEGF agent aflibercept from baseline characteristics. We collected clinical features and optical coherence tomography scans from 1720 eyes of 1612 patients between 2019 and 2021. We evaluated our AI system as a patient selection method by emulating hypothetical clinical trials of different sizes based on our test set. Our method detected up to 57.6% more suboptimal responders than random selection, and up to 24.2% more than any alternative selection criteria tested. Applying this method to the entry process of candidates into randomised controlled trials may contribute to the success of such trials and further inform personalised care.

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FuseMedML: a framework for accelerated discovery in machine learning based biomedicine

Cited by 6 publications

References 9 publications

From Pixels to Diagnosis: Algorithmic Analysis of Clinical Oral Photos for Early Detection of Oral Squamous Cell Carcinoma

From Pixels to Diagnosis: Algorithmic Analysis of Clinical Oral Photos for Early Detection of Oral Squamous Cell Carcinoma

Artificial intelligence-based prediction of overall survival in metastatic renal cell carcinoma

A Multi-Modal AI-Driven Cohort Selection Tool to Predict Suboptimal Non-Responders to Aflibercept Loading-Phase for Neovascular Age-Related Macular Degeneration: PRECISE Study Report 1

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