AIMC Topic: Treatment Outcome

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Automated Identification of Stroke Thrombolysis Contraindications from Synthetic Clinical Notes: A Proof-of-Concept Study.

Cerebrovascular diseases extra
INTRODUCTION: Timely thrombolytic therapy improves outcomes in acute ischemic stroke. Manual chart review to screen for thrombolysis contraindications may be time-consuming and prone to errors. We developed and tested a large language model (LLM)-bas...

Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models.

Nature communications
Orthognathic surgery, or corrective jaw surgery, is performed to correct severe dentofacial deformities and is increasingly sought for cosmetic purposes. Accurate prediction of surgical outcomes is essential for selecting the optimal treatment plan a...

Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy.

Journal for immunotherapy of cancer
BACKGROUND: Accurate prediction of pathologic complete response (pCR) following neoadjuvant immunotherapy combined with chemotherapy (nICT) is crucial for tailoring patient care in esophageal squamous cell carcinoma (ESCC). This study aimed to develo...

Use machine learning to predict treatment outcome of early childhood caries.

BMC oral health
BACKGROUND: Early childhood caries (ECC) is a major oral health problem among preschool children that can significantly influence children's quality of life. Machine learning can accurately predict the treatment outcome but its use in ECC management ...

Establishing a Deep Learning Model That Integrates Pretreatment and Midtreatment Computed Tomography to Predict Treatment Response in Non-Small Cell Lung Cancer.

International journal of radiation oncology, biology, physics
PURPOSE: Patients with identical stages or similar tumor volumes can vary significantly in their responses to radiation therapy (RT) due to individual characteristics, making personalized RT for non-small cell lung cancer (NSCLC) challenging. This st...

Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods.

Tomography (Ann Arbor, Mich.)
RATIONALE: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for patients with LABC before initiating treatment would be valuable to customize therapies and ensure t...

Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer.

Frontiers in immunology
Although the immune checkpoint inhibitors (ICIs) have demonstrated remarkable anti-tumor efficacy in solid tumors, the proportion of ESCC patients who benefit from ICIs remains limited. Current biomarkers have assisted in identifying potential respon...

Artificial intelligence-driven translational medicine: a machine learning framework for predicting disease outcomes and optimizing patient-centric care.

Journal of translational medicine
BACKGROUND: Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient...