Coaxial projective imaging system for surgical navigation and telementoring

Screening of malaria infections in human blood samples with varying parasite densities and anaemic conditions using AI-Powered mid-infrared spectroscopy

BACKGROUND

Effective testing for malaria, including the detection of infections at very low densities, is vital for the successful elimination of the disease. Unfortunately, existing methods are either inexpensive but poorly sensitive or sensitive but costly. Recent studies have shown that mid-infrared spectroscopy coupled with machine learning (MIRs-ML) has potential for rapidly detecting malaria infections but requires further evaluation on diverse samples representative of natural infections in endemic areas. The aim of this study was, therefore, to demonstrate a simple AI-powered, reagent-free, and user-friendly approach that uses mid-infrared spectra from dried blood spots to accurately detect malaria infections across varying parasite densities and anaemic conditions.

METHODS

Plasmodium falciparum strains NF54 and FCR3 were cultured and mixed with blood from 70 malaria-free individuals to create various malaria parasitaemia and anaemic conditions. Blood dilutions produced three haematocrit ratios (50%, 25%, 12.5%) and five parasitaemia levels (6%, 0.1%, 0.002%, 0.00003%, 0%). Dried blood spots were prepared on Whatman™ filter papers and scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) for machine-learning analysis. Three classifiers were trained on an 80%/20% split of 4655 spectra: (I) high contrast (6% parasitaemia vs. negative), (II) low contrast (0.00003% vs. negative) and (III) all concentrations (all positive levels vs. negative). The classifiers were validated with unseen datasets to detect malaria at various parasitaemia levels and anaemic conditions. Additionally, these classifiers were tested on samples from a population survey in malaria-endemic villages of southeastern Tanzania.

RESULTS

The AI classifiers attained over 90% accuracy in detecting malaria infections as low as one parasite per microlitre of blood, a sensitivity unattainable by conventional RDTs and microscopy. These laboratory-developed classifiers seamlessly transitioned to field applicability, achieving over 80% accuracy in predicting natural P. falciparum infections in blood samples collected during the field survey. Crucially, the performance remained unaffected by various levels of anaemia, a common complication in malaria patients.

CONCLUSION

These findings suggest that the AI-driven mid-infrared spectroscopy approach holds promise as a simplified, sensitive and cost-effective method for malaria screening, consistently performing well despite variations in parasite densities and anaemic conditions. The technique simply involves scanning dried blood spots with a desktop mid-infrared scanner and analysing the spectra using pre-trained AI classifiers, making it readily adaptable to field conditions in low-resource settings. In this study, the approach was successfully adapted to field use, effectively predicting natural malaria infections in blood samples from a population-level survey in Tanzania. With additional field trials and validation, this technique could significantly enhance malaria surveillance and contribute to accelerating malaria elimination efforts.

Autofluorescence detection and co-axial projection for intraoperative localization of parathyroid gland

Background

Near-infrared (NIR) autofluorescence detection is an effective method for identifying parathyroid glands (PGs) in thyroidectomy or parathyroidectomy. Fiber optical probes provide quantitative autofluorescence measurements for PG detection owing to its high sensitivity and high excitation light cut-off efficiency at a fixed detection distance. However, an optical fiber probe lacks the imaging capability and cannot map the autofluorescence distribution on top of normal tissue background. Therefore, there is a need for intraoperative mapping of PGs with high sensitivity and imaging resolution.

Methods

We have developed a fluorescence scanning and projection (FSP) system that combines a scanning probe and a co-axial projector for intraoperative localization and in situ display of PGs. Some of the key performance characteristics, including spatial resolution and sensitivity for detection, spatial resolution for imaging, dynamic time latency, and PG localization capability, are characterized and verified by benchtop experiments. Clinical utility of the system is simulated by a fluorescence-guided PG localization surgery on a tissue-simulating phantom and validated in an ex vivo experiment.

Results

The system is able to detect indocyanine green (ICG) solution of 5 pM at a high signal-to-noise ratio (SNR). Additionally, it has a maximal projection error of 0.92 mm, an averaged projection error of 0.5 ± 0.23 mm, and an imaging resolution of 748 μm at a working distance ranging from 35 to 55 cm. The dynamic testing yields a short latency of 153 ± 54 ms, allowing for intraoperative scanning on target tissue during a surgical intervention. The simulated fluorescence-guided PG localization surgery has validated the system’s capability to locate PG phantom with operating room ambient light interference. The simulation experiment on the PG phantom yields a position detection bias of 0.36 ± 0.17 mm, and an area intersection over unit (IoU) of 76.6% ± 6.4%. Fluorescence intensity attenuates exponentially with the thickness of covered tissue over the PG phantom, indicating the need to remove surrounding tissue in order to reveal the weak autofluorescence signal from PGs. The ex vivo experiment demonstrates the technical feasibility of the FSP system for intraoperative PG localization with accuracy.

Conclusion

We have developed a novel probe-based imaging and navigation system with high sensitivity for fluorescence detection, capability for fluorescence image reconstruction, multimodal image fusion and in situ PG display function. Our studies have demonstrated its clinical potential for intraoperative localization and in situ display of PGs in thyroidectomy or parathyroidectomy.

Overcoming the Impact of COVID-19 on Surgical Mentorship: A Scoping Review of Long-distance Mentorship in Surgery

BACKGROUND

Mentorship in the surgical field has been increasingly recognized as a crucial component of career success. Distance mentorship models may be utilized to overcome geographic limitations, increase mentorship access, and strengthen mentoring relationships in surgery.

OBJECTIVE

This review aimed to identify the scope of literature on distance mentoring in surgery, the range of its application, its effectiveness, and any gaps in the literature that should be addressed in order to enhance mentorship in the surgical field.

DESIGN

A comprehensive PubMed review was performed in January 2021 on distance mentorship of students, trainees, and surgeons in the surgical field. Reviews, replies, and non-English articles were excluded. Data was extracted regarding publication year, author's country, specialty, subjects, aim of mentorship model, and efficacy.

RESULTS

134 total studies met inclusion and exclusion criteria. Most studies were published in 2020, written by authors in the United States, from general surgery, and featured an expert surgeon paired with a more junior fully trained surgeon. In all, 93.3% of studies utilized distance mentorship to enhance surgical skill through telementoring and only 4.5% were focused on mentorship to enhance careers through professional development. The remaining studies utilized distance mentorship models to increase surgical research (0.7%) and clinical knowledge (1.5%).

CONCLUSION

The results of this review suggest successful implementation of distance mentoring in surgery through telementoring, but a lack of professionally aimed distance mentorship programs. Amidst COVID-19, distance mentorship is particularly important because of decreased face-to-face opportunity. Future studies in the surgical field should investigate distance mentoring as a means of increasing mentorship for professional development.

Overcoming the Impact of COVID-19 on Surgical Mentorship: A Scoping Review of Long-distance Mentorship in Surgery

BACKGROUND

Mentorship in the surgical field has been increasingly recognized as a crucial component of career success. Distance mentorship models may be utilized to overcome geographic limitations, increase mentorship access, and strengthen mentoring relationships in surgery.

OBJECTIVE

This review aimed to identify the scope of literature on distance mentoring in surgery, the range of its application, its effectiveness, and any gaps in the literature that should be addressed in order to enhance mentorship in the surgical field.

DESIGN

A comprehensive PubMed review was performed in January 2021 on distance mentorship of students, trainees, and surgeons in the surgical field. Reviews, replies, and non-English articles were excluded. Data was extracted regarding publication year, author's country, specialty, subjects, aim of mentorship model, and efficacy.

RESULTS

134 total studies met inclusion and exclusion criteria. Most studies were published in 2020, written by authors in the United States, from general surgery, and featured an expert surgeon paired with a more junior fully trained surgeon. In all, 93.3% of studies utilized distance mentorship to enhance surgical skill through telementoring and only 4.5% were focused on mentorship to enhance careers through professional development. The remaining studies utilized distance mentorship models to increase surgical research (0.7%) and clinical knowledge (1.5%).

CONCLUSION

The results of this review suggest successful implementation of distance mentoring in surgery through telementoring, but a lack of professionally aimed distance mentorship programs. Amidst COVID-19, distance mentorship is particularly important because of decreased face-to-face opportunity. Future studies in the surgical field should investigate distance mentoring as a means of increasing mentorship for professional development.