A Classification Benchmark for Artificial Intelligence Detection of Laryngeal Cancer from Patient Speech

• URL: http://arxiv.org/abs/2412.16267v1
• Date: Fri, 20 Dec 2024 10:34:03 GMT
• Title: A Classification Benchmark for Artificial Intelligence Detection of Laryngeal Cancer from Patient Speech
• Authors: Mary Paterson, James Moor, Luisa Cutillo,
• Abstract summary: Current diagnostic pathways cause many patients to be incorrectly referred to urgent suspected cancer pathways.<n>Artificial intelligence offers a promising solution by enabling non-invasive detection of laryngeal cancer from patient speech.<n>This work introduces a benchmark suite comprising 36 models trained and evaluated on open-source datasets.
• Score: 0.30723404270319693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cases of laryngeal cancer are predicted to rise significantly in the coming years. Current diagnostic pathways cause many patients to be incorrectly referred to urgent suspected cancer pathways, putting undue stress on both patients and the medical system. Artificial intelligence offers a promising solution by enabling non-invasive detection of laryngeal cancer from patient speech, which could help prioritise referrals more effectively and reduce inappropriate referrals of non-cancer patients. To realise this potential, open science is crucial. A major barrier in this field is the lack of open-source datasets and reproducible benchmarks, forcing researchers to start from scratch. Our work addresses this challenge by introducing a benchmark suite comprising 36 models trained and evaluated on open-source datasets. These models are accessible in a public repository, providing a foundation for future research. They evaluate three different algorithms and three audio feature sets, offering a comprehensive benchmarking framework. We propose standardised metrics and evaluation methodologies to ensure consistent and comparable results across future studies. The presented models include both audio-only inputs and multimodal inputs that incorporate demographic and symptom data, enabling their application to datasets with diverse patient information. By providing these benchmarks, future researchers can evaluate their datasets, refine the models, and use them as a foundation for more advanced approaches. This work aims to provide a baseline for establishing reproducible benchmarks, enabling researchers to compare new methods against these standards and ultimately advancing the development of AI tools for detecting laryngeal cancer.

Related papers

• Benchmarking Foundation Speech and Language Models for Alzheimer's Disease and Related Dementia Detection from Spontaneous Speech [14.936023751079654]
  Alzheimer's disease and related dementias are progressive neurodegenerative conditions.<n>Spontaneous speech contains rich acoustic and linguistic markers that may serve as non-invasive biomarkers.<n>Foundation models, pre-trained on large-scale audio or text data, produce high-dimensional embeddings encoding contextual and acoustic features.
  arXiv  Detail & Related papers  (2025-06-09T17:52:31Z)
• Congenital Heart Disease Classification Using Phonocardiograms: A Scalable Screening Tool for Diverse Environments [34.10187730651477]
  Congenital heart disease (CHD) is a critical condition that demands early detection.<n>This study presents a deep learning model designed to detect CHD using phonocardiogram (PCG) signals.<n>We evaluated our model on several datasets, including the primary dataset from Bangladesh.
  arXiv  Detail & Related papers  (2025-03-28T05:47:44Z)
• Development and prospective validation of a prostate cancer detection, grading, and workflow optimization system at an academic medical center [1.9729379339863824]
  We developed models for cancer detection, grading, and screening of cases for IHC ordering.<n>We compared the performance of task-specific prostate models with general-purpose foundation models.<n>No statistically significant differences were observed between task-specific and foundation models in cancer detection.
  arXiv  Detail & Related papers  (2024-10-31T05:29:18Z)
• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas.<n>This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges [2.1835659964186087]
  This paper presents a systematic review of generative models used to synthesize various medical data types. Our study encompasses a broad array of medical data modalities and explores various generative models.
  arXiv  Detail & Related papers  (2024-06-27T14:00:11Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring.<n>As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs.<n>The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Zero-shot and Few-shot Generation Strategies for Artificial Clinical Records [1.338174941551702]
  This study assesses the capability of the Llama 2 LLM to create synthetic medical records that accurately reflect real patient information. We focus on generating synthetic narratives for the History of Present Illness section, utilising data from the MIMIC-IV dataset for comparison. Our findings suggest that this chain-of-thought prompted approach allows the zero-shot model to achieve results on par with those of fine-tuned models, based on Rouge metrics evaluation.
  arXiv  Detail & Related papers  (2024-03-13T16:17:09Z)
• README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP [9.432205523734707]
  We introduce a new task of automatically generating lay definitions, aiming to simplify medical terms into patient-friendly lay language. We first created the dataset, an extensive collection of over 50,000 unique (medical term, lay definition) pairs and 300,000 mentions. We have also engineered a data-centric Human-AI pipeline that synergizes data filtering, augmentation, and selection to improve data quality.
  arXiv  Detail & Related papers  (2023-12-24T23:01:00Z)
• The Limits of Fair Medical Imaging AI In The Wild [43.97266228706059]
  We investigate the extent to which medical AI utilizes demographic encodings. We confirm that medical imaging AI leverages demographic shortcuts in disease classification. We find that models with less encoding of demographic attributes are often most "globally optimal"
  arXiv  Detail & Related papers  (2023-12-11T18:59:50Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
  "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
  arXiv  Detail & Related papers  (2023-07-30T16:08:45Z)
• Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance [49.87607548975686]
  The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis. We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents. Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-06-02T01:46:31Z)
• Generative models improve fairness of medical classifiers under distribution shifts [49.10233060774818]
  We show that learning realistic augmentations automatically from data is possible in a label-efficient manner using generative models. We demonstrate that these learned augmentations can surpass ones by making models more robust and statistically fair in- and out-of-distribution.
  arXiv  Detail & Related papers  (2023-04-18T18:15:38Z)
• Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review [1.832300121391956]
  Methods: A search of PubMed, Scopus, Web of Science, CENTRAL, and WHO-ICTRP was conducted. Risk of bias was assessed using PROBAST. There were 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 models with other diagnostically relevant outcomes. All models were found to be at high or unclear risk of bias overall, with most research having a high risk of bias in the analysis.
  arXiv  Detail & Related papers  (2023-03-31T12:26:29Z)
• Sparse Dynamical Features generation, application to Parkinson's Disease diagnosis [0.0]
  We propose a new approach inspired by the functioning of the brain that uses the dynamics, frequency and temporal content of EEGs to extract new demarcating features of the disease. The method was evaluated on a publicly available dataset containing EEG signals recorded during a 3-oddball auditory task involving N = 50 subjects, of whom 25 suffer from PD.
  arXiv  Detail & Related papers  (2022-10-20T22:39:29Z)
• Exploring linguistic feature and model combination for speech recognition based automatic AD detection [61.91708957996086]
  Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques. Scarcity of specialist data leads to uncertainty in both model selection and feature learning when developing such systems. This paper investigates the use of feature and model combination approaches to improve the robustness of domain fine-tuning of BERT and Roberta pre-trained text encoders.
  arXiv  Detail & Related papers  (2022-06-28T05:09:01Z)
• Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection [62.23830810096617]
  Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care to delay further progression. This paper presents the development of a state-of-the-art Conformer based speech recognition system built on the DementiaBank Pitt corpus for automatic AD detection.
  arXiv  Detail & Related papers  (2022-06-23T12:50:55Z)
• Recommendations on test datasets for evaluating AI solutions in pathology [2.001521933638504]
  AI solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance and obtain regulatory approval. A committee of various stakeholders, including commercial AI developers, pathologists, and researchers, discussed key aspects and conducted extensive literature reviews on test datasets in pathology.
  arXiv  Detail & Related papers  (2022-04-21T14:52:47Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence [79.038671794961]
  We launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK.
  arXiv  Detail & Related papers  (2021-11-18T00:43:41Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)
• Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach [55.41644538483948]
  This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units. The aim is to support decision making addressed at reducing the incidence rate of infections.
  arXiv  Detail & Related papers  (2020-05-07T16:13:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.