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Khizir L, Bhandari V, Kaloth S, Pfail J, Lichtbroun B, Yanamala N, Elsamra SE. From Diagnosis to Precision Surgery: The Transformative Role of Artificial Intelligence in Urologic Imaging. J Endourol 2024; 38:824-835. [PMID: 38888003 DOI: 10.1089/end.2023.0695] [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: 06/20/2024] Open
Abstract
The multidisciplinary nature of artificial intelligence (AI) has allowed for rapid growth of its application in medical imaging. Artificial intelligence algorithms can augment various imaging modalities, such as X-rays, CT, and MRI, to improve image quality and generate high-resolution three-dimensional images. AI reconstruction of three-dimensional models of patient anatomy from CT or MRI scans can better enable urologists to visualize structures and accurately plan surgical approaches. AI can also be optimized to create virtual reality simulations of surgical procedures based on patient-specific data, giving urologists more hands-on experience and preparation. Recent development of artificial intelligence modalities, such as TeraRecon and Ceevra, offer rapid and efficient medical imaging analyses aimed at enhancing the provision of urologic care, notably for intraoperative guidance during robot-assisted radical prostatectomy (RARP) and partial nephrectomy.
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Affiliation(s)
- Labeeqa Khizir
- Division of Urology, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | | | - Srivarsha Kaloth
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - John Pfail
- Division of Urology, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Benjamin Lichtbroun
- Division of Urology, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Naveena Yanamala
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Sammy E Elsamra
- Division of Urology, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
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Kumar RP, Sivan V, Bachir H, Sarwar SA, Ruzicka F, O'Malley GR, Lobo P, Morales IC, Cassimatis ND, Hundal JS, Patel NV. Can Artificial Intelligence Mitigate Missed Diagnoses by Generating Differential Diagnoses for Neurosurgeons? World Neurosurg 2024; 187:e1083-e1088. [PMID: 38759788 DOI: 10.1016/j.wneu.2024.05.052] [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] [Received: 12/18/2023] [Revised: 05/08/2024] [Accepted: 05/09/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND/OBJECTIVE Neurosurgery emphasizes the criticality of accurate differential diagnoses, with diagnostic delays posing significant health and economic challenges. As large language models (LLMs) emerge as transformative tools in healthcare, this study seeks to elucidate their role in assisting neurosurgeons with the differential diagnosis process, especially during preliminary consultations. METHODS This study employed 3 chat-based LLMs, ChatGPT (versions 3.5 and 4.0), Perplexity AI, and Bard AI, to evaluate their diagnostic accuracy. Each LLM was prompted using clinical vignettes, and their responses were recorded to generate differential diagnoses for 20 common and uncommon neurosurgical disorders. Disease-specific prompts were crafted using Dynamed, a clinical reference tool. The accuracy of the LLMs was determined based on their ability to identify the target disease within their top differential diagnoses correctly. RESULTS For the initial differential, ChatGPT 3.5 achieved an accuracy of 52.63%, while ChatGPT 4.0 performed slightly better at 53.68%. Perplexity AI and Bard AI demonstrated 40.00% and 29.47% accuracy, respectively. As the number of considered differentials increased from 2 to 5, ChatGPT 3.5 reached its peak accuracy of 77.89% for the top 5 differentials. Bard AI and Perplexity AI had varied performances, with Bard AI improving in the top 5 differentials at 62.11%. On a disease-specific note, the LLMs excelled in diagnosing conditions like epilepsy and cervical spine stenosis but faced challenges with more complex diseases such as Moyamoya disease and amyotrophic lateral sclerosis. CONCLUSIONS LLMs showcase the potential to enhance diagnostic accuracy and decrease the incidence of missed diagnoses in neurosurgery.
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Affiliation(s)
- Rohit Prem Kumar
- Department of Neurosurgery, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA.
| | - Vijay Sivan
- Department of Neurosurgery, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA
| | - Hanin Bachir
- Department of Neurosurgery, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA
| | - Syed A Sarwar
- Department of Neurosurgery, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA
| | - Francis Ruzicka
- Department of Neurosurgery, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA
| | - Geoffrey R O'Malley
- Department of Neurosurgery, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA
| | - Paulo Lobo
- Department of Neurosurgery, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA
| | - Ilona Cazorla Morales
- Department of Neurosurgery, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA
| | - Nicholas D Cassimatis
- Department of Neurosurgery, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA
| | - Jasdeep S Hundal
- Department of Neurology, HMH-Jersey Shore University Medical Center, Neptune, New Jersey, USA
| | - Nitesh V Patel
- Department of Neurosurgery, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA; Department of Neurosurgery, HMH-Jersey Shore University Medical Center, Neptune, New Jersey, USA
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Crouzet A, Lopez N, Riss Yaw B, Lepelletier Y, Demange L. The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang? Molecules 2024; 29:2716. [PMID: 38930784 PMCID: PMC11206022 DOI: 10.3390/molecules29122716] [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] [Received: 03/29/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.
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Affiliation(s)
- Aurore Crouzet
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
| | - Nicolas Lopez
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
- ENOES, 62 Rue de Miromesnil, 75008 Paris, France
- Unité Mixte de Recherche «Institut de Physique Théorique (IPhT)» CEA-CNRS, UMR 3681, Bat 774, Route de l’Orme des Merisiers, 91191 St Aubin-Gif-sur-Yvette, France
| | - Benjamin Riss Yaw
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
| | - Yves Lepelletier
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
- Université Paris Cité, Imagine Institute, 24 Boulevard Montparnasse, 75015 Paris, France
- INSERM UMR 1163, Laboratory of Cellular and Molecular Basis of Normal Hematopoiesis and Hematological Disorders: Therapeutical Implications, 24 Boulevard Montparnasse, 75015 Paris, France
| | - Luc Demange
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
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Deol ES, Tollefson MK, Antolin A, Zohar M, Bar O, Ben-Ayoun D, Mynderse LA, Lomas DJ, Avant RA, Miller AR, Elliott DS, Boorjian SA, Wolf T, Asselmann D, Khanna A. Automated surgical step recognition in transurethral bladder tumor resection using artificial intelligence: transfer learning across surgical modalities. Front Artif Intell 2024; 7:1375482. [PMID: 38525302 PMCID: PMC10958784 DOI: 10.3389/frai.2024.1375482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 02/26/2024] [Indexed: 03/26/2024] Open
Abstract
Objective Automated surgical step recognition (SSR) using AI has been a catalyst in the "digitization" of surgery. However, progress has been limited to laparoscopy, with relatively few SSR tools in endoscopic surgery. This study aimed to create a SSR model for transurethral resection of bladder tumors (TURBT), leveraging a novel application of transfer learning to reduce video dataset requirements. Materials and methods Retrospective surgical videos of TURBT were manually annotated with the following steps of surgery: primary endoscopic evaluation, resection of bladder tumor, and surface coagulation. Manually annotated videos were then utilized to train a novel AI computer vision algorithm to perform automated video annotation of TURBT surgical video, utilizing a transfer-learning technique to pre-train on laparoscopic procedures. Accuracy of AI SSR was determined by comparison to human annotations as the reference standard. Results A total of 300 full-length TURBT videos (median 23.96 min; IQR 14.13-41.31 min) were manually annotated with sequential steps of surgery. One hundred and seventy-nine videos served as a training dataset for algorithm development, 44 for internal validation, and 77 as a separate test cohort for evaluating algorithm accuracy. Overall accuracy of AI video analysis was 89.6%. Model accuracy was highest for the primary endoscopic evaluation step (98.2%) and lowest for the surface coagulation step (82.7%). Conclusion We developed a fully automated computer vision algorithm for high-accuracy annotation of TURBT surgical videos. This represents the first application of transfer-learning from laparoscopy-based computer vision models into surgical endoscopy, demonstrating the promise of this approach in adapting to new procedure types.
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Affiliation(s)
- Ekamjit S. Deol
- Department of Urology, Mayo Clinic, Rochester, MN, United States
| | | | | | - Maya Zohar
- theator.io, Palo Alto, CA, United States
| | - Omri Bar
- theator.io, Palo Alto, CA, United States
| | | | | | - Derek J. Lomas
- Department of Urology, Mayo Clinic, Rochester, MN, United States
| | - Ross A. Avant
- Department of Urology, Mayo Clinic, Rochester, MN, United States
| | - Adam R. Miller
- Department of Urology, Mayo Clinic, Rochester, MN, United States
| | | | | | - Tamir Wolf
- theator.io, Palo Alto, CA, United States
| | | | - Abhinav Khanna
- Department of Urology, Mayo Clinic, Rochester, MN, United States
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Stark M, Mynbaev O, Malvasi A, Tinelli A. Revolutionizing patient care: the harmonious blend of artificial intelligence and surgical tradition. INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL PATHOLOGY 2024; 17:47-50. [PMID: 38455508 PMCID: PMC10915289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 01/23/2024] [Indexed: 03/09/2024]
Abstract
Surgery has undergone remarkable evolution over the past decades, propelled by unprecedented technological advancement. Despite these changes, the role of surgeons and their irreplaceable qualities remains pivotal. This article delves into the intersection of surgery and artificial intelligence (AI), underscoring the enduring significance of human expertise and values. The potential of AI to learn and improve over time holds great promise for enhancing various facets of surgery, including diagnostics, personalized treatment, preoperative planning, real-time support in the operating room, and comprehensive postoperative analytics of the outcome. However, it is essential to emphasize the continued importance of the surgeon's role to uphold universal surgical principles. This includes a commitment to minimalism and the use of evidence-based practice, ensuring optimal outcomes and standardized procedures. By recognizing the synergies between AI and traditional surgical approaches, we can navigate the evolving landscape of surgery to achieve the highest standards of patient care.
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Affiliation(s)
- Michael Stark
- The New European Surgical Academy10117 Berlin, Germany
| | - Ospan Mynbaev
- The New European Surgical Academy10117 Berlin, Germany
| | - Antonio Malvasi
- The New European Surgical Academy10117 Berlin, Germany
- Department of Biomedical Sciences and Human Oncology, University of Bari70121 Bari, Italy
| | - Andrea Tinelli
- The New European Surgical Academy10117 Berlin, Germany
- Department of Obstetrics and Gynecology and CERICSAL (CEntro di RIcerca Clinico SALentino), Veris delli Ponti HospitalScorrano, 73020 Lecce, Italy
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Zhai Y, Chen Z, Zheng Z, Wang X, Yan X, Liu X, Yin J, Wang J, Zhang J. Artificial intelligence for automatic surgical phase recognition of laparoscopic gastrectomy in gastric cancer. Int J Comput Assist Radiol Surg 2024; 19:345-353. [PMID: 37914911 DOI: 10.1007/s11548-023-03027-5] [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: 03/05/2023] [Accepted: 10/02/2023] [Indexed: 11/03/2023]
Abstract
PURPOSE This study aimed to classify laparoscopic gastric cancer phases. We also aimed to develop a transformer-based artificial intelligence (AI) model for automatic surgical phase recognition and to evaluate the model's performance using laparoscopic gastric cancer surgical videos. METHODS One hundred patients who underwent laparoscopic surgery for gastric cancer were included in this study. All surgical videos were labeled and classified into eight phases (P0. Preparation. P1. Separate the greater gastric curvature. P2. Separate the distal stomach. P3. Separate lesser gastric curvature. P4. Dissect the superior margin of the pancreas. P5. Separation of the proximal stomach. P6. Digestive tract reconstruction. P7. End of operation). This study proposed an AI phase recognition model consisting of a convolutional neural network-based visual feature extractor and temporal relational transformer. RESULTS A visual and temporal relationship network was proposed to automatically perform accurate surgical phase prediction. The average time for all surgical videos in the video set was 9114 ± 2571 s. The longest phase was at P1 (3388 s). The final research accuracy, F1, recall, and precision were 90.128, 87.04, 87.04, and 87.32%, respectively. The phase with the highest recognition accuracy was P1, and that with the lowest accuracy was P2. CONCLUSION An AI model based on neural and transformer networks was developed in this study. This model can identify the phases of laparoscopic surgery for gastric cancer accurately. AI can be used as an analytical tool for gastric cancer surgical videos.
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Affiliation(s)
- Yuhao Zhai
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China
- State Key Lab of Digestive Health, Beijing, China
| | - Zhen Chen
- Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong SAR, China
| | - Zhi Zheng
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China
- State Key Lab of Digestive Health, Beijing, China
| | - Xi Wang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China
- State Key Lab of Digestive Health, Beijing, China
| | - Xiaosheng Yan
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China
- State Key Lab of Digestive Health, Beijing, China
| | - Xiaoye Liu
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China
- State Key Lab of Digestive Health, Beijing, China
| | - Jie Yin
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China
- State Key Lab of Digestive Health, Beijing, China
| | - Jinqiao Wang
- Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong SAR, China.
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Haidian District, Beijing, China.
- Wuhan AI Research, Wuhan, China.
| | - Jun Zhang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China.
- State Key Lab of Digestive Health, Beijing, China.
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Kovačević N, Hočevar M, Vivod G, Merlo S. Vascular and Urinary Tract Anatomic Variants Relevant to Para-Aortic Lymphadenectomy in Women with Gynecological Cancers. Cancers (Basel) 2023; 15:4959. [PMID: 37894326 PMCID: PMC10605252 DOI: 10.3390/cancers15204959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Para-aortic lymphadenectomy is an essential part of gynecologic oncologic surgical treatment. The surgeon should be aware of the complex usual anatomy and its common variants. METHODS Between January 2021 and May 2023, 58 women underwent para-aortic lymphadenectomy for gynecologic malignancies. RESULTS Vascular and urinary tract anatomic variants were retrospectively reviewed from the prospective institutional database and results were compared with preoperative contrast-enhanced abdominal CT. Of these 58 women, 47 women had no vascular or urinary tract variants. One woman had a double inferior vena cava, two patients were found to have a retro-aortic left renal vein, four had accessory renal arteries, two had a double left ureter, one had a ptotic kidney in the iliac fossa, and one patient had bilateral kidney malrotation. Anatomic variants in the preoperative CT were described by a radiologist in only two patients, and additional vascular and urinary tract variants were found incidentally at the time of surgery. CONCLUSIONS Acknowledgment of vascular and urinary tract variants is helpful for the surgeon to establish an appropriate surgical plan and to avoid iatrogenic surgical trauma.
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Affiliation(s)
- Nina Kovačević
- Department of Gynecological Oncology, Institute of Oncology Ljubljana, 1000 Ljubljana, Slovenia; (N.K.); (M.H.); (G.V.)
- Faculty of Medicine, University of Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
- Faculty of Health Care Angela Boškin, Spodnji Plavž 3, 4270 Jesenice, Slovenia
| | - Marko Hočevar
- Department of Gynecological Oncology, Institute of Oncology Ljubljana, 1000 Ljubljana, Slovenia; (N.K.); (M.H.); (G.V.)
- Faculty of Medicine, University of Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
| | - Gregor Vivod
- Department of Gynecological Oncology, Institute of Oncology Ljubljana, 1000 Ljubljana, Slovenia; (N.K.); (M.H.); (G.V.)
- Faculty of Medicine, University of Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
| | - Sebastjan Merlo
- Department of Gynecological Oncology, Institute of Oncology Ljubljana, 1000 Ljubljana, Slovenia; (N.K.); (M.H.); (G.V.)
- Faculty of Medicine, University of Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
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Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics 2023; 15:1916. [PMID: 37514102 PMCID: PMC10385763 DOI: 10.3390/pharmaceutics15071916] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/28/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.
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Affiliation(s)
- Lalitkumar K Vora
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK
| | - Amol D Gholap
- Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Palghar 401404, Maharashtra, India
| | - Keshava Jetha
- Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
- Ph.D. Section, Gujarat Technological University, Ahmedabad 382424, Gujarat, India
| | | | - Hetvi K Solanki
- Pharmacy Section, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
| | - Vivek P Chavda
- Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
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Jalal NA, Alshirbaji TA, Docherty PD, Arabian H, Laufer B, Krueger-Ziolek S, Neumuth T, Moeller K. Laparoscopic Video Analysis Using Temporal, Attention, and Multi-Feature Fusion Based-Approaches. SENSORS (BASEL, SWITZERLAND) 2023; 23:1958. [PMID: 36850554 PMCID: PMC9964851 DOI: 10.3390/s23041958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
Adapting intelligent context-aware systems (CAS) to future operating rooms (OR) aims to improve situational awareness and provide surgical decision support systems to medical teams. CAS analyzes data streams from available devices during surgery and communicates real-time knowledge to clinicians. Indeed, recent advances in computer vision and machine learning, particularly deep learning, paved the way for extensive research to develop CAS. In this work, a deep learning approach for analyzing laparoscopic videos for surgical phase recognition, tool classification, and weakly-supervised tool localization in laparoscopic videos was proposed. The ResNet-50 convolutional neural network (CNN) architecture was adapted by adding attention modules and fusing features from multiple stages to generate better-focused, generalized, and well-representative features. Then, a multi-map convolutional layer followed by tool-wise and spatial pooling operations was utilized to perform tool localization and generate tool presence confidences. Finally, the long short-term memory (LSTM) network was employed to model temporal information and perform tool classification and phase recognition. The proposed approach was evaluated on the Cholec80 dataset. The experimental results (i.e., 88.5% and 89.0% mean precision and recall for phase recognition, respectively, 95.6% mean average precision for tool presence detection, and a 70.1% F1-score for tool localization) demonstrated the ability of the model to learn discriminative features for all tasks. The performances revealed the importance of integrating attention modules and multi-stage feature fusion for more robust and precise detection of surgical phases and tools.
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Affiliation(s)
- Nour Aldeen Jalal
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany
| | - Tamer Abdulbaki Alshirbaji
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany
| | - Paul David Docherty
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand
| | - Herag Arabian
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
| | - Bernhard Laufer
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
| | - Sabine Krueger-Ziolek
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
| | - Thomas Neumuth
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany
| | - Knut Moeller
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand
- Department of Microsystems Engineering, University of Freiburg, 79110 Freiburg, Germany
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A Systematic Review of Artificial Intelligence Applications in Plastic Surgery: Looking to the Future. Plast Reconstr Surg Glob Open 2022; 10:e4608. [PMID: 36479133 PMCID: PMC9722565 DOI: 10.1097/gox.0000000000004608] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 08/24/2022] [Indexed: 01/25/2023]
Abstract
UNLABELLED Artificial intelligence (AI) is presently employed in several medical specialties, particularly those that rely on large quantities of standardized data. The integration of AI in surgical subspecialties is under preclinical investigation but is yet to be widely implemented. Plastic surgeons collect standardized data in various settings and could benefit from AI. This systematic review investigates the current clinical applications of AI in plastic and reconstructive surgery. METHODS A comprehensive literature search of the Medline, EMBASE, Cochrane, and PubMed databases was conducted for AI studies with multiple search terms. Articles that progressed beyond the title and abstract screening were then subcategorized based on the plastic surgery subspecialty and AI application. RESULTS The systematic search yielded a total of 1820 articles. Forty-four studies met inclusion criteria warranting further analysis. Subcategorization of articles by plastic surgery subspecialties revealed that most studies fell into aesthetic and breast surgery (27%), craniofacial surgery (23%), or microsurgery (14%). Analysis of the research study phase of included articles indicated that the current research is primarily in phase 0 (discovery and invention; 43.2%), phase 1 (technical performance and safety; 27.3%), or phase 2 (efficacy, quality improvement, and algorithm performance in a medical setting; 27.3%). Only one study demonstrated translation to clinical practice. CONCLUSIONS The potential of AI to optimize clinical efficiency is being investigated in every subfield of plastic surgery, but much of the research to date remains in the preclinical status. Future implementation of AI into everyday clinical practice will require collaborative efforts.
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Li Z, Koban KC, Schenck TL, Giunta RE, Li Q, Sun Y. Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends. J Clin Med 2022; 11:jcm11226826. [PMID: 36431301 PMCID: PMC9693628 DOI: 10.3390/jcm11226826] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/24/2022] [Accepted: 10/28/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique nature of dermatology, AI-aided dermatological diagnosis based on image recognition has become a modern focus and future trend. Key scientific concepts of review: The use of 3D imaging systems allows clinicians to screen and label skin pigmented lesions and distributed disorders, which can provide an objective assessment and image documentation of lesion sites. Dermatoscopes combined with intelligent software help the dermatologist to easily correlate each close-up image with the corresponding marked lesion in the 3D body map. In addition, AI in the field of prosthetics can assist in the rehabilitation of patients and help to restore limb function after amputation in patients with skin tumors. THE AIM OF THE STUDY For the benefit of patients, dermatologists have an obligation to explore the opportunities, risks and limitations of AI applications. This study focuses on the application of emerging AI in dermatology to aid clinical diagnosis and treatment, analyzes the current state of the field and summarizes its future trends and prospects so as to help dermatologists realize the impact of new technological innovations on traditional practices so that they can embrace and use AI-based medical approaches more quickly.
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Affiliation(s)
- Zhouxiao Li
- Department of Plastic and Reconstructive Surgery, Shanghai 9th People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200023, China
- Division of Hand, Plastic and Aesthetic Surgery, University Hospital, LMU Munich, 80339 Munich, Germany
| | | | - Thilo Ludwig Schenck
- Division of Hand, Plastic and Aesthetic Surgery, University Hospital, LMU Munich, 80339 Munich, Germany
| | - Riccardo Enzo Giunta
- Division of Hand, Plastic and Aesthetic Surgery, University Hospital, LMU Munich, 80339 Munich, Germany
| | - Qingfeng Li
- Department of Plastic and Reconstructive Surgery, Shanghai 9th People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200023, China
- Correspondence: (Q.L.); (Y.S.)
| | - Yangbai Sun
- Department of Plastic and Reconstructive Surgery, Shanghai 9th People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200023, China
- Correspondence: (Q.L.); (Y.S.)
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Irani CSS, Chu CH. Evolving with technology: Machine learning as an opportunity for operating room nurses to improve surgical care-A commentary. J Nurs Manag 2022; 30:3802-3805. [PMID: 35816560 DOI: 10.1111/jonm.13736] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 07/03/2022] [Indexed: 12/30/2022]
Abstract
AIMS To describe machine learning applications in an operating room setting, raise awareness of the lack of nursing inclusion on machine learning algorithm development, and show how operating room nurses can co-create this new technology. BACKGROUND Operating room nurses and managers perform anticipatory work on a daily basis to manage intrinsic and extrinsic factors that can cause surgical delays. EVALUATION Recent literature on machine learning and its potential use in operating room settings was reviewed along with literature on the role of the nurse in co-creating novel technology. KEY ISSUE Machine learning technology is rapidly evolving and being created for the operating room environment to improve patient safety and flow. Operating room nurses and managers are not being included in the development of machine learning algorithms, meaning products may be created that are not usable for all members of the surgical team. CONCLUSION This commentary highlights the ways machine learning effectively assists nurses and nursing managers, suggesting a pathway forward for surgical nursing as co-creators and implementers. IMPLICATION FOR NURSING MANAGEMENT Nursing managers will be exposed to machine learning programmes in the near future and need to understand the benefits they have for patient safety and patient flow.
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Affiliation(s)
- Cameron S S Irani
- Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada
| | - Charlene H Chu
- Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada.,KITE- Toronto Rehab Institution, University Health Network, Toronto, Ontario, Canada
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13
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Harnessing Artificial Intelligence in Maxillofacial Surgery. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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14
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Mazaheri S, Loya MF, Newsome J, Lungren M, Gichoya JW. Challenges of Implementing Artificial Intelligence in Interventional Radiology. Semin Intervent Radiol 2021; 38:554-559. [PMID: 34853501 DOI: 10.1055/s-0041-1736659] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Artificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a subcategory of machine learning that takes advantage of multiple layers of interconnected neurons capable of analyzing immense amounts of data and "learning" patterns and offering predictions. It appears to be poised to fundamentally transform and help advance the field of diagnostic radiology, as heralded by numerous published use cases and number of FDA-cleared products. On the other hand, while multiple publications have touched upon many great hypothetical use cases of AI in interventional radiology (IR), the actual implementation of AI in IR clinical practice has been slow compared with the diagnostic world. In this article, we set out to examine a few challenges contributing to this scarcity of AI applications in IR, including inherent specialty challenges, regulatory hurdles, intellectual property, raising capital, and ethics. Owing to the complexities involved in implementing AI in IR, it is likely that IR will be one of the late beneficiaries of AI. In the meantime, it would be worthwhile to continuously engage in defining clinically relevant use cases and focus our limited resources on those that would benefit our patients the most.
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Affiliation(s)
- Sina Mazaheri
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Mohammed F Loya
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Janice Newsome
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia.,Department of Interventional Radiology, Emory University School of Medicine, Atlanta, Georgia
| | - Mathew Lungren
- LPCH Pediatric Interventional Radiology, Stanford University, Stanford, California
| | - Judy Wawira Gichoya
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
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Solanki SL, Pandrowala S, Nayak A, Bhandare M, Ambulkar RP, Shrikhande SV. Artificial intelligence in perioperative management of major gastrointestinal surgeries. World J Gastroenterol 2021; 27:2758-2770. [PMID: 34135552 PMCID: PMC8173379 DOI: 10.3748/wjg.v27.i21.2758] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 04/06/2021] [Accepted: 04/28/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms. The purpose of this review was to summarize concepts, the scope, applications, and limitations in major gastrointestinal surgery. This is a narrative review of the available literature on the key capabilities of AI to help anesthesiologists, surgeons, and other physicians to understand and critically evaluate ongoing and new AI applications in perioperative management. AI uses available databases called “big data” to formulate an algorithm. Analysis of other data based on these algorithms can help in early diagnosis, accurate risk assessment, intraoperative management, automated drug delivery, predicting anesthesia and surgical complications and postoperative outcomes and can thus lead to effective perioperative management as well as to reduce the cost of treatment. Perioperative physicians, anesthesiologists, and surgeons are well-positioned to help integrate AI into modern surgical practice. We all need to partner and collaborate with data scientists to collect and analyze data across all phases of perioperative care to provide clinical scenarios and context. Careful implementation and use of AI along with real-time human interpretation will revolutionize perioperative care, and is the way forward in future perioperative management of major surgery.
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Affiliation(s)
- Sohan Lal Solanki
- Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Saneya Pandrowala
- Gastro-Intestinal Services, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Abhirup Nayak
- Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Manish Bhandare
- Gastro-Intestinal Services, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Reshma P Ambulkar
- Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Shailesh V Shrikhande
- Gastro-Intestinal Services, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
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Pereira KR. Harnessing Artificial Intelligence in Maxillofacial Surgery. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_322-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Wilhelm D, Padoy N. Artificial Intelligence in Medicine: Passing Hype or the Holy Grail of Solutions? Visc Med 2020; 36:425-427. [PMID: 33447597 PMCID: PMC7768117 DOI: 10.1159/000511429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 09/03/2020] [Indexed: 11/19/2022] Open
Affiliation(s)
- Dirk Wilhelm
- Technical University of Munich, Faculty of Medicine, Klinikum rechts der Isar, Department of Surgery, Munich, Germany
| | - Nicolas Padoy
- Research Group CAMMA, University of Strasbourg, Strasbourg, France
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