Comparing code-free and bespoke deep learning approaches in ophthalmology.

AIM: Code-free deep learning (CFDL) allows clinicians without coding expertise to build high-quality artificial intelligence (AI) models without writing code. In this review, we comprehensively review the advantages that CFDL offers over bespoke expert-designed deep learning (DL). As exemplars, we use the following tasks: (1) diabetic retinopathy screening, (2) retinal multi-disease classification, (3) surgical video classification, (4) oculomics and (5) resource management. METHODS: We performed a search for studies reporting CFDL applications in ophthalmology in MEDLINE (through PubMed) from inception to June 25, 2023, using the keywords 'autoML' AND 'ophthalmology'. After identifying 5 CFDL studies looking at our target tasks, we performed a subsequent search to find corresponding bespoke DL studies focused on the same tasks. Only English-written articles with full text available were included. Reviews, editorials, protocols and case reports or case series were excluded. We identified ten relevant studies for this review. RESULTS: Overall, studies were optimistic towards CFDL's advantages over bespoke DL in the five ophthalmological tasks. However, much of such discussions were identified to be mono-dimensional and had wide applicability gaps. High-quality assessment of better CFDL applicability over bespoke DL warrants a context-specific, weighted assessment of clinician intent, patient acceptance and cost-effectiveness. We conclude that CFDL and bespoke DL are unique in their own assets and are irreplaceable with each other. Their benefits are differentially valued on a case-to-case basis. Future studies are warranted to perform a multidimensional analysis of both techniques and to improve limitations of suboptimal dataset quality, poor applicability implications and non-regulated study designs. CONCLUSION: For clinicians without DL expertise and easy access to AI experts, CFDL allows the prototyping of novel clinical AI systems. CFDL models concert with bespoke models, depending on the task at hand. A multidimensional, weighted evaluation of the factors involved in the implementation of those models for a designated task is warranted.

Do we need a co-pilot in the operating theatre? A cross-sectional study on surgeons' perceptions.

OBJECTIVE: The aim of this original study was to investigate general surgeons' perceptions on the role of dual surgeon operating for high-risk, elective complex surgical procedures. MATERIAL AND METHODS: A 21-part cross-sectional online survey was self-completed by 85 general surgeons across hospitals in the UK. The survey assessed the perception of dual surgeon operating on patient morbidity and mortality, surgeons' burnout rates, complaints, patient waiting times and overall trainee experience. Statistical analysis was performed using R version 3.6.1. RESULTS: Overall 78.8% believed that dual surgeon operating could help to overcome these human factors. Sub-analysis includes improve surgeon fatigue (89.4%), improve confidence (83.5%), improve decision-making (76.5%), minimise technical error (69.4%), improve communication, team work and leadership skills (65.9%). 65.9% believed it would reduce surgeons' burnout. There was a statistically significant relationship between the participants believing it would reduce surgeons' burnout and those who felt it would reduce complaints, Chi-squared(4) = [30.8], p = [0.00000342]. A statistically significant relationship was noted between participants believing it would reduce surgeons' burnout and those who felt it would reduce patient mortality/morbidity, Chi-squared(4) = [19.9], p = [0.000517]. CONCLUSION: The survey has highlighted positive surgeons' perceptions regarding dual surgeon operating.

A foundation model for generalizable disease detection from retinal images.

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.

Third-Generation Single-Molecule Sequencing for Preimplantation Genetic Testing of Aneuploidy and Segmental Imbalances.

BACKGROUND: Current strategies for preimplantation genetic testing for aneuploidy or structural rearrangements (PGT-A/SR) rely mainly on next-generation sequencing (NGS) and microarray platforms, which are robust but require expensive instrumentation. We explored the suitability of third-generation single-molecule sequencing as a PGT-A/SR screening platform for both aneuploidy and segmental imbalance. METHODS: Single-cell and multicell replicates from aneuploid or segmentally unbalanced cell lines (n = 208) were SurePlex-amplified, randomized, and subjected to (a) Nanopore-based single-molecule sequencing (Oxford Nanopore Technologies) and (b) NGS using a leading commercial PGT-A solution (Illumina VeriSeq PGS). Archival SurePlex-amplified trophectoderm biopsy samples (n = 96) previously analyzed using the commercial kit were blinded and reanalyzed using Nanopore. RESULTS: Nanopore-based PGT-A identified the specific aberration in 95.45% (84/88) and 97.78% (88/90) of single-/multicells with an aneuploidy or segmental imbalance (10-30.5 Mb), respectively. Comparison against the commercial kit's results revealed concordances of 98.86% (87/88) and 98.89% (89/90) for the aneuploid and segmentally unbalanced (10-30.5 Mb aberration) samples, respectively. Detection sensitivity for smaller segmental imbalances (5-5.8 Mb aberration, n = 30) decreased markedly on both platforms. Nanopore-based PGT-A reanalysis of trophectoderm biopsy samples was 97.92% (94/96) concordant with the commercial kit results. CONCLUSION: Up to 24 SurePlex-amplified single-cell, multicell, or trophectoderm samples could be sequenced in a single MinION flow-cell for subsequent preimplantation genetic testing for aneuploidy or structural rearrangements (PGT-A/SR) analysis, with results obtainable in ≤3 days and at per-sample costs that are competitive with commercial offerings. Nanopore's third-generation single-molecule sequencing represents a viable alternative to current commercial NGS-based PGT-A solutions for aneuploidy and segmental imbalance (≥10 Mb) screening of single-/multicell or trophectoderm biopsy samples.

Association Between Retinal Features From Multimodal Imaging and Schizophrenia.

Importance: The potential association of schizophrenia with distinct retinal changes is of clinical interest but has been challenging to investigate because of a lack of sufficiently large and detailed cohorts. Objective: To investigate the association between retinal biomarkers from multimodal imaging (oculomics) and schizophrenia in a large real-world population. Design, Setting, and Participants: This cross-sectional analysis used data from a retrospective cohort of 154 830 patients 40 years and older from the AlzEye study, which linked ophthalmic data with hospital admission data across England. Patients attended Moorfields Eye Hospital, a secondary care ophthalmic hospital with a principal central site, 4 district hubs, and 5 satellite clinics in and around London, United Kingdom, and had retinal imaging during the study period (January 2008 and April 2018). Data were analyzed from January 2022 to July 2022. Main Outcomes and Measures: Retinovascular and optic nerve indices were computed from color fundus photography. Macular retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (mGC-IPL) thicknesses were extracted from optical coherence tomography. Linear mixed-effects models were used to examine the association between schizophrenia and retinal biomarkers. Results: A total of 485 individuals (747 eyes) with schizophrenia (mean [SD] age, 64.9 years [12.2]; 258 [53.2%] female) and 100 931 individuals (165 400 eyes) without schizophrenia (mean age, 65.9 years [13.7]; 53 253 [52.8%] female) were included after images underwent quality control and potentially confounding conditions were excluded. Individuals with schizophrenia were more likely to have hypertension (407 [83.9%] vs 49 971 [48.0%]) and diabetes (364 [75.1%] vs 28 762 [27.6%]). The schizophrenia group had thinner mGC-IPL (-4.05 µm, 95% CI, -5.40 to -2.69; P = 5.4 × 10-9), which persisted when investigating only patients without diabetes (-3.99 µm; 95% CI, -6.67 to -1.30; P = .004) or just those 55 years and younger (-2.90 µm; 95% CI, -5.55 to -0.24; P = .03). On adjusted analysis, retinal fractal dimension among vascular variables was reduced in individuals with schizophrenia (-0.14 units; 95% CI, -0.22 to -0.05; P = .001), although this was not present when excluding patients with diabetes. Conclusions and Relevance: In this study, patients with schizophrenia had measurable differences in neural and vascular integrity of the retina. Differences in retinal vasculature were mostly secondary to the higher prevalence of diabetes and hypertension in patients with schizophrenia. The role of retinal features as adjunct outcomes in patients with schizophrenia warrants further investigation.

Simultaneous Screening of the FRAXA and FRAXE Loci for Rapid Detection of FMR1 CGG and/or AFF2 CCG Repeat Expansions by Triplet-Primed PCR.

Moderate to hyper-expansion of trinucleotide repeats at the FRAXA and FRAXE fragile sites, with or without concurrent hypermethylation, has been associated with intellectual disability and other conditions. Unlike molecular diagnosis of FMR1 CGG repeat expansions in FRAXA, current detection of AFF2 CCG repeat expansions in FRAXE relies on low-throughput and otherwise inefficient techniques combining Southern blot analysis and PCR. A novel triplet-primed PCR assay was developed for simultaneous screening for trinucleotide repeat expansions at the FRAXA and FRAXE fragile sites, and was validated using archived clinical samples of known FMR1 and AFF2 genotypes. Population samples and FRAXE-affected samples were sequenced for the evaluation of variations in the AFF2 CCG repeat structure. The duplex assay accurately identified expansions at the FMR1 and AFF2 trinucleotide repeat loci. On Sanger sequencing of the AFF2 CCG repeat, the single-nucleotide polymorphism variant rs868914124(C) that effectively adds two CCG repeats at the 5'-end, was enriched in the Malay population and with short repeats (<11 CCGs), and was present in all six expanded AFF2 alleles of this study. All expanded AFF2 alleles contained multiple non-CCG interruptions toward the 5'-end of the repeat. A sensitive, robust, and rapid assay has been developed for the simultaneous detection of expansion mutations at the FMR1 and AFF2 trinucleotide repeat loci, simplifying screening for FRAXA- and FRAXE-associated disorders.