基于多模态影像的机器学习与深度学习在帕金森病诊治中的应用进展

帕金森病是一种常见的神经系统退行性疾病,在出现临床症状时已经不可逆,所以早期诊断显得尤为重要。随着人工智能技术的发展,机器学习与深度学习已广泛用于医学数据分析和神经影像自动化诊断研究。本文将综述基于多模态影像的机器学习与深度学习在帕金森病早期诊断、分类研究以及功能预测中的应用情况,并对其局限性作出阐述,以及对未来的发展趋势进行展望。     

基于深度学习的人工智能技术在腮腺肿瘤诊治的实践及思考

基于深度学习的人工智能技术已被广泛应用于计算机视觉领域,在医学图像处理方面,基于卷积的深度学习神经网络具备较好的智能学习和目标区域关键信息分析处理能力,在各类医学影像的图像分割实践中表现出近似于甚至高于专业人员的智能水平。腮腺是唾液腺肿瘤好发的腺体,腮腺肿瘤是口腔颌面外科的常见病和多发病,对腮腺肿瘤的精准诊疗仍存在临床挑战。本研究围绕深度学习技术在腮腺肿瘤智能诊疗的应用和前景作一述评,希冀推动口腔智慧医疗的进一步深化及发展。     

深度学习算法在青光眼筛查与诊断中应用的研究进展

近年来,深度学习算法已成为计算机科学研究的重要前沿领域,并在图像识别、语音识别及自然语言处理等领域得到广泛应用,但其与医疗领域的融合起步较晚。将深度学习算法用于眼底照相、光学相干断层扫描及视野检查等眼部常规检查中可有效地评估青光眼、白内障、年龄相关性黄斑变性及糖尿病性视网膜病变等常见致盲性眼病的风险。目前,该算法在眼科常见疾病筛查与诊断中的实用性已得到充分证明。本文中笔者从青光眼筛查与诊断的角度就该算法在其中应用的研究进展进行综述。     

Deep learning in optical coherence tomography: Where are the gaps?

Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, interpreting OCT images requires experts in both OCT images and eye diseases since many factors such as artefacts and concomitant diseases can affect the accuracy of quantitative measurements made by post-processing algorithms. Currently, there is a growing interest in applying deep learning (DL) methods to analyse OCT images automatically. This review summarises the trends in DL-based OCT image analysis in ophthalmology, discusses the current gaps, and provides potential research directions. DL in OCT analysis shows promising performance in several tasks: (1) layers and features segmentation and quantification; (2) disease classification; (3) disease progression and prognosis; and (4) referral triage level prediction. Different studies and trends in the development of DL-based OCT image analysis are described and the following challenges are identified and described: (1) public OCT data are scarce and scattered; (2) models show performance discrepancies in real-world settings; (3) models lack of transparency; (4) there is a lack of societal acceptance and regulatory standards; and (5) OCT is still not widely available in underprivileged areas. More work is needed to tackle the challenges and gaps, before DL is further applied in OCT image analysis for clinical use.