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Wolffsohn JS, Berkow D, Chan KY, Chaurasiya SK, Fadel D, Haddad M, Imane T, Jones L, Sheppard AL, Vianya-Estopa M, Walsh K, Woods J, Zeri F, Morgan PB. BCLA CLEAR Presbyopia: Evaluation and diagnosis. Cont Lens Anterior Eye 2024; 47:102156. [PMID: 38641525 DOI: 10.1016/j.clae.2024.102156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Abstract
It is important to be able to measure the range of clear focus in clinical practice to advise on presbyopia correction techniques and to optimise the correction power. Both subjective and objective techniques are necessary: subjective techniques (such as patient reported outcome questionnaires and defocus curves) assess the impact of presbyopia on a patient and how the combination of residual objective accommodation and their natural DoF work for them; objective techniques (such as autorefraction, corneal topography and lens imaging) allow the clinician to understand how well a technique is working optically and whether it is the right choice or how adjustments can be made to optimise performance. Techniques to assess visual performance and adverse effects must be carefully conducted to gain a reliable end-point, considering the target size, contrast and illumination. Objective techniques are generally more reliable, can help to explain unexpected subjective results and imaging can be a powerful communication tool with patients. A clear diagnosis, excluding factors such as binocular vision issues or digital eye strain that can also cause similar symptoms, is critical for the patient to understand and adapt to presbyopia. Some corrective options are more permanent, such as implanted inlays / intraocular lenses or laser refractive surgery, so the optics can be trialled with contact lenses in advance (including differences between the eyes) to better communicate with the patient how the optics will work for them so they can make an informed choice.
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Affiliation(s)
- James S Wolffsohn
- School of Optometry, Health and Life Sciences, Aston University, Birmingham, United Kingdom.
| | - David Berkow
- Department of Ophthalmology, Rambam Health Care Campus, Haifa, Israel
| | - Ka Yin Chan
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong
| | - Suraj K Chaurasiya
- Department of Contact Lens and Anterior Segment, CL Gupta Eye Institute, Moradabad, India; Department of Optometry and Vision Science, CL Gupta Eye Institute, Moradabad, India
| | - Daddi Fadel
- Centre for Ocular Research & Education (CORE), School of Optometry & Vision Science, University of Waterloo, Waterloo, Canada
| | - Mera Haddad
- Faculty of Applied Medical Sciences, Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Tarib Imane
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, College of Medicine, University of Illinois at Chicago, United States
| | - Lyndon Jones
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong; Centre for Ocular Research & Education (CORE), School of Optometry & Vision Science, University of Waterloo, Waterloo, Canada
| | - Amy L Sheppard
- School of Optometry, Health and Life Sciences, Aston University, Birmingham, United Kingdom
| | - Marta Vianya-Estopa
- Vision and Hearing Research Centre, Anglia Ruskin University, Cambridge, United Kingdom
| | - Karen Walsh
- CooperVision Inc., San Ramon, CA, United States
| | - Jill Woods
- Centre for Ocular Research & Education (CORE), School of Optometry & Vision Science, University of Waterloo, Waterloo, Canada
| | - Fabrizio Zeri
- School of Optometry, Health and Life Sciences, Aston University, Birmingham, United Kingdom; University of Milano-Bicocca, Department of Materials Science, Milan, Italy
| | - Philip B Morgan
- Eurolens Research, Division of Pharmacy and Optometry, University of Manchester, United Kingdom
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Ali R, Connolly ID, Tang OY, Mirza FN, Johnston B, Abdulrazeq HF, Lim RK, Galamaga PF, Libby TJ, Sodha NR, Groff MW, Gokaslan ZL, Telfeian AE, Shin JH, Asaad WF, Zou J, Doberstein CE. Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach. NPJ Digit Med 2024; 7:63. [PMID: 38459205 PMCID: PMC10923794 DOI: 10.1038/s41746-024-01039-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 02/14/2024] [Indexed: 03/10/2024] Open
Abstract
Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impede patients' comprehension. This study investigates the use of GPT-4 to simplify surgical consent forms and introduces an AI-human expert collaborative approach to validate content appropriateness. Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4's potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. Analysis of 15 academic medical centers' consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P < 0.05) following GPT-4-faciliated simplification. Readability improved from an average college freshman to an 8th-grade level (P = 0.004), matching the average American's reading level. Medical and legal sufficiency consistency was confirmed. GPT-4 generated procedure-specific consent forms for five varied surgical procedures at an average 6th-grade reading level. These forms received perfect scores on a standardized consent form rubric and withstood scrutiny upon expert subspeciality surgeon review. This study demonstrates the first AI-human expert collaboration to enhance surgical consent forms, significantly improving readability without sacrificing clinical detail. Our framework could be extended to other patient communication materials, emphasizing clear communication and mitigating disparities related to health literacy barriers.
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Affiliation(s)
- Rohaid Ali
- Department of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown University, Providence, RI, USA.
- Norman Prince Neurosciences Institute, Providence, RI, USA.
| | - Ian D Connolly
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Oliver Y Tang
- Department of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Fatima N Mirza
- Department of Dermatology, The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Benjamin Johnston
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Hael F Abdulrazeq
- Department of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown University, Providence, RI, USA
- Norman Prince Neurosciences Institute, Providence, RI, USA
| | - Rachel K Lim
- Department of Surgery & Division of Cardiothoracic Surgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | | | - Tiffany J Libby
- Department of Dermatology, The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Neel R Sodha
- Department of Surgery & Division of Cardiothoracic Surgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Michael W Groff
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Ziya L Gokaslan
- Department of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown University, Providence, RI, USA
- Norman Prince Neurosciences Institute, Providence, RI, USA
| | - Albert E Telfeian
- Department of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown University, Providence, RI, USA
- Norman Prince Neurosciences Institute, Providence, RI, USA
| | - John H Shin
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Wael F Asaad
- Department of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - James Zou
- Departments of Electrical Engineering, Biomedical Data Science, and Computer Science, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Curtis E Doberstein
- Department of Neurosurgery, Rhode Island Hospital and The Warren Alpert Medical School of Brown University, Providence, RI, USA
- Norman Prince Neurosciences Institute, Providence, RI, USA
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Lee TF, Hsieh YW, Yang PY, Tseng CH, Lee SH, Yang J, Chang L, Wu JM, Tseng CD, Chao PJ. Using meta-analysis and CNN-NLP to review and classify the medical literature for normal tissue complication probability in head and neck cancer. Radiat Oncol 2024; 19:5. [PMID: 38195582 PMCID: PMC10775485 DOI: 10.1186/s13014-023-02381-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 11/20/2023] [Indexed: 01/11/2024] Open
Abstract
PURPOSE The study aims to enhance the efficiency and accuracy of literature reviews on normal tissue complication probability (NTCP) in head and neck cancer patients using radiation therapy. It employs meta-analysis (MA) and natural language processing (NLP). MATERIAL AND METHODS The study consists of two parts. First, it employs MA to assess NTCP models for xerostomia, dysphagia, and mucositis after radiation therapy, using Python 3.10.5 for statistical analysis. Second, it integrates NLP with convolutional neural networks (CNN) to optimize literature search, reducing 3256 articles to 12. CNN settings include a batch size of 50, 50-200 epoch range and a 0.001 learning rate. RESULTS The study's CNN-NLP model achieved a notable accuracy of 0.94 after 200 epochs with Adamax optimization. MA showed an AUC of 0.67 for early-effect xerostomia and 0.74 for late-effect, indicating moderate to high predictive accuracy but with high variability across studies. Initial CNN accuracy of 66.70% improved to 94.87% post-tuning by optimizer and hyperparameters. CONCLUSION The study successfully merges MA and NLP, confirming high predictive accuracy for specific model-feature combinations. It introduces a time-based metric, words per minute (WPM), for efficiency and highlights the utility of MA and NLP in clinical research.
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Affiliation(s)
- Tsair-Fwu Lee
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC
- Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan, ROC
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan, ROC
| | - Yang-Wei Hsieh
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC
| | - Pei-Ying Yang
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC
| | - Chi-Hung Tseng
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC
| | - Shen-Hao Lee
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC
- Department of Radiation Oncology, Linkou Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Linkou, Taiwan, ROC
| | - Jack Yang
- Medical Physics at Monmouth Medical Center, Barnabas Health Care at Long Branch, Long Branch, NJ, USA
| | - Liyun Chang
- Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung, 840, Taiwan, ROC
| | - Jia-Ming Wu
- Heavy Ion Center of Wuwei Cancer Hospital, Gansu Wuwei Academy of Medical Sciences, Gansu Wuwei Tumor Hospital, Wuwei, Gansu Province, China
- Department of Medical Physics, Chengde Medical University, Chengde, Hebei Province, China
| | - Chin-Dar Tseng
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC
| | - Pei-Ju Chao
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC.
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