Detecting panitumumab/FOLFOX responders in K-Ras wild-type metastatic colorectal cancer through an artificial intelligence-based analytical tool

Collective Intelligence, Medicine, Oncology, Publications

Background: Patients with K-RAS wild-type (WT) metastatic colorectal cancer (mCRC) are currently considered the optimal candidates for upfront treatment with combinations of chemotherapy and EGFR inhibitors. These combinations significantly extend overall survival (OS) compared to chemotherapy alone. However, a proportion of patients would not achieve this goal. This study has investigated the ability of an…

Use of deep learning frameworks to detect super-responder and super-survivor stage IV squamous non-small-cell lung cancer (NSCLC) patients treated with a gemcitabine and cisplatin combination

Collective Intelligence, Medicine, Oncology, Publications

Background: Synthetic fingerprints integrate clinical data within computational models allowing the identification of particular clinical subpopulations at a given moment. We here describe a deep learning strategy to detect super-responder and super-survivor patients with squamous NSCLC by setting up synthetic fingerprints and using unsupervised deep learning frameworks (UDLF). Methods: Through www.projectdatasphere.org, we accessed the control…

Predicting disease progression and mortality in metastatic colorectal cancer patients (mCRC) through an artificial intelligence-based analytical tool

Collective Intelligence, Medicine, Oncology, Publications

Background: Predicting the clinical course of metastatic disease remains a key challenge in CRC. Estimating prognosis of these late-stage patients can avoid undertreatment or overtreatment and also guide the follow-up intensity. This study has investigated the ability of an artificial intelligence-based analytical tool to identify those mCRC patients with high risk of disease progression and…

Deep Neural Frameworks Improve the Accuracy of General Practitioners in the Classification of Pigmented Skin Lesions

Collective Intelligence, Dermatology, Medicine, Oncology, Publications

This study evaluated whether deep learning frameworks trained in large datasets can help non-dermatologist physicians improve their accuracy in categorizing the seven most common pigmented skin lesions. Open-source skin images were downloaded from the International Skin Imaging Collaboration (ISIC) archive. Different deep neural networks (DNNs) (n = 8) were trained based on a random dataset…

Artificial intelligence: a disruptive tool for a smarter medicine

Collective Intelligence, Medicine, Publications

Although highly successful, the medical R&D model is failing at improving people’s health due to a series of flaws and defects inherent to the model itself. A new collective intelligence, incorporating human and artificial intelligence (AI) could overcome these obstacles. Because AI will play a key role in this new collective intelligence, it is necessary…

Robust COVID-19-related condition classification network

COVID, Publications

COVID-19 can exponentially precipitate life-threatening emergencies as witnessed during the recent spreading of a novel coronavirus infection which can rapidly evolve into lung collapse and respiratory distress (among other various severe clinical conditions). Our study evaluates the performance of a tailor-designed deep convolutional network on the tasks of early detection and localization of radiological signs…

Deep neural frameworks improve the accuracy of general practitioners in the classification of pigmented skin lesions

Dermatology, Publications

Artificial intelligence can be a key tool in the context of assisting in the diagnosis of dermatological conditions, particularly when performed by general practitioners with limited or no access to high resolution optical equipment. This study evaluates the performance of deep convolutional neural networks (DNNs) in the classification of seven pigmented skin lesions. Additionally, it…